Best Time Series Analysis Courses

Find the best online Time Series Analysis Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Time Series Analysis Courses.

Time Series Analysis and Forecasting using Python

Learn about time series analysis & forecasting models in Python |Time Data Visualization|AR|MA|ARIMA|Regression| ANN

Created by Start-Tech Academy - 3,000,000+ Enrollments | 4+ Rated | 160+ Countries

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Students: 108928, Price: $29.99

Students: 108928, Price:  Paid

You're looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right?

You've found the right Time Series Forecasting and Time Series Analysis course using Python Time Series techniques. This course teaches you everything you need to know about different time series forecasting and time series analysis models and how to implement these models in Python time series.

After completing this course you will be able to:

  • Implement time series forecasting and time series analysis models such as AutoRegression, Moving Average, ARIMA, SARIMA etc.

  • Implement multivariate time series forecasting models based on Linear regression and Neural Networks.

  • Confidently practice, discuss and understand different time series forecasting, time series analysis models and Python time series techniques used by organizations

How will this course help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Time Series Forecasting course on time series analysis and Python time series applications.

If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it. You will also learn time series forecasting models, time series analysis and Python time series techniques.

Why should you choose this course?

We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts through how-to examples. Each section has the following components:

  • Theoretical concepts and use cases of different forecasting models, time series forecasting and time series analysis

  • Step-by-step instructions on implement time series forecasting models in Python

  • Downloadable Code files containing data and solutions used in each lecture on time series forecasting, time series analysis and Python time series techniques

  • Class notes and assignments to revise and practice the concepts on time series forecasting, time series analysis and Python time series techniques

The practical classes where we create the model for each of these strategies is something which differentiates this course from any other available online course on time series forecasting, time series analysis and Python time series techniques.

.What makes us qualified to teach you?

  • The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this course. They also have an in-depth knowledge on time series forecasting, time series analysis and Python time series techniques.

We are also the creators of some of the most popular online courses - with over 170,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman - Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on time series forecasting, time series analysis and Python time series techniques.

Each section contains a practice assignment for you to practically implement your learning on time series forecasting, time series analysis and Python time series techniques.

What is covered in this course?

Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will deal with time series forecasting, time series analysis and Python time series techniques. We will also explore how one can use forecasting models to

  • See patterns in time series data

  • Make forecasts based on models

Let me give you a brief overview of the course

  • Section 1 - Introduction

In this section we will learn about the course structure and how the concepts on time series forecasting, time series analysis and Python time series techniques will be taught in this course.

  • Section 2 - Python basics

This section gets you started with Python.

This section will help you set up the python and Jupyter environment on your system and it'll teach

you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

The basics taught in this part will be fundamental in learning time series forecasting, time series analysis and Python time series techniques on later part of this course.

  • Section 3 - Basics of Time Series Data

In this section, we will discuss about the basics of time series data, application of time series forecasting, and the standard process followed to build a forecasting model, time series forecasting, time series analysis and Python time series techniques.

  • Section 4 - Pre-processing Time Series Data

In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models and execute time series forecasting, time series analysis and implement Python time series techniques.

  • Section 5 - Getting Data Ready for Regression Model

In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important.

We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation.

  • Section 6 - Forecasting using Regression Model

This section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.

We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results.

  • Section 7 - Theoretical Concepts

This part will give you a solid understanding of concepts involved in Neural Networks.

In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

  • Section 8 - Creating Regression and Classification ANN model in Python

In this part you will learn how to create ANN models in Python.

We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

I am pretty confident that the course will give you the necessary knowledge and skills related to time series forecasting, time series analysis and Python time series techniques to immediately see practical benefits in your work place.

Go ahead and click the enroll button, and I'll see you in lesson 1 of this course on time series forecasting, time series analysis and Python time series techniques!

Cheers

Start-Tech Academy

Time Series Analysis Real World Projects in Python

Learn how to Solve 3 real Business Problems. Build Robust AI ,Time Series Models for Time Series Analysis & Forecasting

Created by Shan Singh - Data Scientist

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Students: 60761, Price: $19.99

Students: 60761, Price:  Paid

Are you looking to land a top-paying job in Data Science , AI & Time Series Analysis & Forecasting?

Or are you a seasoned AI practitioner who want to take your career to the next level?

Or are you an aspiring data scientist who wants to get Hands-on  Data Science and Time Series Analysis?

If the answer is yes to any of these questions, then this course is for you!

This course will teach you the practical skills that would allow you to land a job as a quantitative financial analyst, a data analyst or a data scientist.

In business, Data Science , AI  is applied to optimize business processes, maximize revenue and reduce cost. The purpose of this course is to provide you with knowledge of key aspects of data science & Time Series applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets.

Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!

This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.

1.Task #1 @Predicting the Temperature  : Develop an Time Series model to predict Temperature..

3.Task #2 @Predict Covid-19 Cases: Develop Time Series Model using Prophet that can predict Covid-19 cases

2.Task #3 @Predict the Stock Prices: Predict the prices of stock using Time Series Algorithms.

Python for Time Series Data Analysis

Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!

Created by Jose Portilla - Head of Data Science, Pierian Data Inc.

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Students: 28428, Price: $109.99

Students: 28428, Price:  Paid

Welcome to the best online resource for learning how to use the Python programming Language for Time Series Analysis!

This course will teach you everything you need to know to use Python for forecasting time series data to predict new future data points.

We'll start off with the basics by teaching you how to work with and manipulate data using the NumPy and Pandas libraries with Python. Then we'll dive deeper into working with Pandas by learning about visualizations with the Pandas library and how to work with time stamped data with Pandas and Python.

Then we'll begin to learn about the statsmodels library and its powerful built in Time Series Analysis Tools. Including learning about Error-Trend-Seasonality decomposition and basic Holt-Winters methods.

Afterwards we'll get to the heart of the course, covering general forecasting models. We'll talk about creating AutoCorrelation and Partial AutoCorrelation charts and using them in conjunction with powerful ARIMA based models, including Seasonal ARIMA models and SARIMAX to include Exogenous data points.

Afterwards we'll learn about state of the art Deep Learning techniques with Recurrent Neural Networks that use deep learning to forecast future data points.

This course even covers Facebook's Prophet library, a simple to use, yet powerful Python library developed to forecast into the future with time series data.

So what are you waiting for! Learn how to work with your time series data and forecast the future!

We'll see you inside the course!

Introduction to Time Series Analysis and Forecasting in R

Work with time series and all sorts of time related data in R - Forecasting, Time Series Analysis, Predictive Analytics

Created by R-Tutorials Training - Data Science Education

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Students: 11062, Price: $99.99

Students: 11062, Price:  Paid

Understand the Now – Predict the Future!

Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to

  • see patterns in time series data
  • model this data
  • finally make forecasts based on those models

Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!

  • What will you learn in this course and how is it structured?

You will learn about different ways in how you can handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package and especially the lubridate package.

You will learn how to visualize, clean and prepare your data. Data preparation takes a huge part of your time as an analyst. Knowing the best functions for outlier detection, missing value imputation and visualization can safe your day.

After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests.

Then you will see how different models work, how they are set up in R and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied with plenty of exercises.

  • Where are those methods applied?

In nearly any quantitatively working field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied.

  • Is it hard to understand and learn those methods?

Unfortunately learning material on Time Series Analysis Programming in R is quite technical and needs tons of prior knowledge to be understood.

With this course it is the goal to make understanding modeling and forecasting as intuitive and simple as possible for you.

While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with time data on a regular basis can benefit from this course.

  • How do I prepare best to benefit from this course?

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (course R Basics).

What R you waiting for?

Time Series Analysis in Python. Master Applied Data Analysis

Python Time Series Analysis with 10+ Forecasting Models including ARIMA, SARIMA, Regression & Time Series Data Analysis

Created by Data Is Good Academy - An Google, Facebook, Kaggle Grandmasters team

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Students: 10093, Price: $19.99

Students: 10093, Price:  Paid

The Ultimate Course on Time Series Analysis in Python brings you expertise in Forecasting Models, Regression, ARIMA, SARIMA and Time Series Data Analysis with Python

Do you want to know how meteorologists forecast weather?

Do you want to know how retailers reduce excess inventory and increase profit margin?

Predict the future using Time Series Forecasting!

Time series forecasting is all about looking into the future.

Time Series is an important field in statistical programming. It allows you to analyze:-

1. Trends

2. Seasonality

3. Irregularity

Time Series Analysis has tons of applications such as stock market analysis, pattern recognition, earthquake prediction, census analysis and many more.

Due to the advanced modern technologies, the data is growing exponentially and this data can be used to modelled for the future which can really make a big difference.

You are at the right place!

Welcome to this online resource to learn Time Series Analysis using Python.

This course will really help you to boost your career.

This course begins with the basic level and goes up to the most advanced techniques step by step. Even if you do not know anything about time series, this course will make complete sense to you.

In this course you will learn about the following:-

1. What is time series data, its applications and components.

2. Fetching time series data using different methods.

3. Handling missing values and outliers in time series data.

4. Decomposing and splitting time series data.

5. Different smoothing techniques such as simple moving averages, simple exponential, holt, and holt-winter exponential.

6. Checking stationarity of the time series data and converting non-stationary to stationary.

7. Auto-regressive models such as simple AR model and moving average model.

8. Advanced auto-regressive models such as ARMA, ARIMA, SARIMA.

9. ARIMAX and SARIMAX model.

10. Evaluation metrics used for time series data.

11. Rules for choosing the right model for time series data.

All the mentioned topics will be covered theoretically as well as implemented in code.

You will compare all the models and will see how to read the results.

We will work with real data and you will have access to all the resources used in this course.

This course is for everyone who wants to master time series and become proficient in working with real-life time-based data.

For taking up this course you need to have prior knowledge of Python programming.

But wait!

Here is the surprise!!

If you are not aware of the python programming language then also don't worry.

We have a crash course in python for you. You can take up python's crash course and then proceed with the time series analysis.

Time Series Analysis in Python 2021

Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting

Created by 365 Careers - Creating opportunities for Business & Finance students

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Students: 9254, Price: $89.99

Students: 9254, Price:  Paid

How does a commercial bank forecast the expected performance of their loan portfolio?

Or how does an investment manager estimate a stock portfolio’s risk?

Which are the quantitative methods used to predict real-estate properties?

If there is some time dependency, then you know it - the answer is: time series analysis.

This course will teach you the practical skills that would allow you to land a job as a quantitative finance analyst, a data analyst or a data scientist.

In no time, you will acquire the fundamental skills that will enable you to perform complicated time series analysis directly applicable in practice. We have created a time series course that is not only timeless but also:

· Easy to understand

· Comprehensive

· Practical

· To the point

· Packed with plenty of exercises and resources

But we know that may not be enough.

We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…

Welcome to Time Series Analysis in Python!

The big question in taking an online course is what to expect. And we’ve made sure that you are provided with everything you need to become proficient in time series analysis.

We start by exploring the fundamental time series theory to help you understand the modeling that comes afterwards.

Then throughout the course, we will work with a number of Python libraries, providing you with a complete training. We will use the powerful time series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, StatsModels, yfinance, ARCH and pmdarima.

With these tools we will master the most widely used models out there:

· AR (autoregressive model)

· MA (moving-average model)

· ARMA (autoregressive-moving-average model)

· ARIMA (autoregressive integrated moving average model)

· ARIMAX (autoregressive integrated moving average model with exogenous variables)

. SARIA (seasonal autoregressive moving average model)

. SARIMA (seasonal autoregressive integrated moving average model)

. SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)

· ARCH (autoregressive conditional heteroscedasticity model)

· GARCH (generalized autoregressive conditional heteroscedasticity model)

. VARMA (vector autoregressive moving average model)

We know that time series is one of those topics that always leaves some doubts.

Until now.

This course is exactly what you need to comprehend time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes, quiz questions, and many, many exercises – everything is included.

What you get?

· Active Q&A support

· Supplementary materials – notebook files, course notes, quiz questions, exercises

· All the knowledge to get a job with time series analysis

· A community of data science enthusiasts

· A certificate of completion

· Access to future updates

· Solve real-life business cases that will get you the job

We are happy to offer a 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and start mastering time series in Python today.

Forecasting and Time Series Analysis in Tableau

Use Tableau to work with time series, generate forecasts and even add R functionality to enhance Tableau.

Created by R-Tutorials Training - Data Science Education

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Students: 4419, Price: $94.99

Students: 4419, Price:  Paid

Do you want to know how to handle time series in Tableau?

Do you want to use Tableaus forecasting feature to get great visualizations?

Or you probably want to know how to add extra forecasting features like ARIMA models to Tableau?

Time based data has its own rules and implications. We will discuss these in Tableau. Quite often time series data is used to look into the future. Forecasting is the name of the game here. Luckily Tableau offers an exponential smoothing forecasting tool, which we will of course explore.

Sometimes you might find that Tableau's internal forecasting tools are too limited. Well, for these instances I will show you how to integrate the R forecast package into Tableau to do ARIMA modeling. This whole process is so well implemented that it can be done without prior R knowledge. In one of the last sections I will show you how it’s done, step by step.

So what are you going to learn in the course?

We start with the general knowledge you need to work with time series data. Especially data roles. We will then discuss moving averages which are widely used in time series analysis. And we will do some time based filtering. We will of course write our own functions, we will create parameters and you will also learn about the time based functions Tableau has to offer. Each of these things will be enforced with exercises.

That is the first section, after that we will be forecasting with Tableau, that means exponential smoothing. I will show you how to read the results you get and how to manually modify the forecast settings.

That is one way of forecasting in Tableau, but there is an advanced alternative. You can use R from within Tableau to perform advanced forecast modeling as we will learn in the last section of the course.

So how do you best prepare for this course?

Well, I built the course for people who already know a bit about Tableau. You should be able to get data into Tableau and to orient yourself in the interface. You should know the basics already. That way we can focus on time series and forecasting and we do not waste precious time on basic things you might already know.

You do not need R skills, although it is an advantage. The methods outlined in the last section are explained in a way so that you can follow along easily.

I hope you will enjoy this course - do not forget to add it to your CV, so that human resources knows that you train yourself on the latest technologies. Valuable skills are definitely a career booster.

Complete Time Series Analysis With Python

Python Time Series: How To Use Data Science, Statistics & Machine Learning For Modelling Time Series Data in Python

Created by Minerva Singh - Bestselling Instructor & Data Scientist(Cambridge Uni)

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Students: 4237, Price: $89.99

Students: 4237, Price:  Paid

THIS IS YOUR COMPLETE GUIDE TO TIME SERIES DATA ANALYSIS IN PYTHON!

This course is your complete guide to time series analysis using Python. So, all the  main aspects of analyzing temporal data will be covered n depth..

If you take this course, you can do away with taking other courses or buying books on Python based data analysis.  

In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in in analysing time series data in Python, you can give your company a competitive edge and boost your career to the next level.

                                                       

LEARN FROM AN EXPERT DATA SCIENTIST  WITH +5 YEARS OF EXPERIENCE:

Hey, my name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources  using data science related techniques and i have produced many publications for international peer reviewed journals.

 Over the course of my research I realised almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic .

So, unlike other instructors, I dig deep into the data science features of R and gives you a one-of-a-kind grounding in data science related topics!

You will go all the way from carrying out data reading & cleaning  to to finally implementing powerful statistical and machine learning algorithms for analyzing time series data.

Among other things:

  • You will be introduced to powerful Python-based packages for time series analysis.

  • You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for time series data.

  • & you will learn to apply these frameworks to real life data including temporal stocks and financial data.  

NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED!

You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

My course will help you implement the methods using REAL DATA obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real-life.

After taking this course, you’ll easily use the common time series packages in Python...

You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.

We will work with real data and you will have access to all the code and data used in the course. 

JOIN MY COURSE NOW!

Applied Time Series Analysis and Forecasting with R Projects

Use R to work on real world time series analysis and forecasting examples. Applied data science with R.

Created by R-Tutorials Training - Data Science Education

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Students: 3853, Price: $89.99

Students: 3853, Price:  Paid

Welcome to the world of R and Time Series Analysis!

At the moment R is the leading open source software for time series analysis and forecasting. No other tool, not even python, comes close to the functions and features available in R. Things like exponential smoothing, ARIMA models, time series cross validation, missing data handling, visualizations and forecasts are easily accessible in R and its add on packages. Therefore, R is the right choice for time series analysis and this course gives you an opportunity to train and practice it.

So how is the course structured?

This is a hands on course with 3 distinct projects to solve! Each project has a main topic and a secondary topic. Both are discussed on real world data. In the first project you work with trending data, and as a secondary topic you will learn how to create standard and ggplot2 time series visualizations. The dataset for that project will be an employment rate dataset.

The second project with the German monthly inflation rates over the last 10 years shows how to model seasonal datasets. And you will also compare the models with time series cross validation.

In the third project you will connect R to yahoo finance and scrape stock data. The resulting data requires loads of pre-processing and cleaning including missing data imputation. Once we prepared the data, we will check out which weekday is the best for buying and selling the Novartis stock.

You should know some R to be able to follow along. There is for example the introduction to time series analysis and forecasting course. That course is more a step by step guide while this one is an applied and project based one. Both courses can be taken on their own, or you take a look at both and learn the subject from 2 different angles.

As always you will get the course script as a text file. Of course you get all the standard Udemy benefits like 30 days money back guarantee, lifetime access, instructor support and a certificate for your CV.

Financial Engineering and Artificial Intelligence in Python

Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE!

Created by Lazy Programmer Team - Artificial Intelligence and Machine Learning Engineer

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Students: 3670, Price: $199.99

Students: 3670, Price:  Paid

Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering?

Today, you can stop imagining, and start doing.

This course will teach you the core fundamentals of financial engineering, with a machine learning twist.

We will cover must-know topics in financial engineering, such as:

  • Exploratory data analysis, significance testing, correlations, alpha and beta

  • Time series analysis, simple moving average, exponentially-weighted moving average

  • Holt-Winters exponential smoothing model

  • ARIMA and SARIMA

  • Efficient Market Hypothesis

  • Random Walk Hypothesis

  • Time series forecasting ("stock price prediction")

  • Modern portfolio theory

  • Efficient frontier / Markowitz bullet

  • Mean-variance optimization

  • Maximizing the Sharpe ratio

  • Convex optimization with Linear Programming and Quadratic Programming

  • Capital Asset Pricing Model (CAPM)

  • Algorithmic trading (VIP only)

  • Statistical Factor Models (VIP only)

  • Regime Detection with Hidden Markov Models (VIP only)

In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as:

  • Regression models

  • Classification models

  • Unsupervised learning

  • Reinforcement learning and Q-learning

***VIP-only sections (get it while it lasts!) ***

  • Algorithmic trading (trend-following, machine learning, and Q-learning-based strategies)

  • Statistical factor models

  • Regime detection and modeling volatility clustering with HMMs

We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense. It is a lesson in how not to apply AI in finance.

As the author of ~30 courses in machine learning, deep learning, data science, and artificial intelligence, I couldn't help but wander into the vast and complex world of financial engineering.

This course is for anyone who loves finance or artificial intelligence, and especially if you love both!

Whether you are a student, a professional, or someone who wants to advance their career - this course is for you.

Thanks for reading, I will see you in class!

Suggested Prerequisites:

  • Matrix arithmetic

  • Probability

  • Decent Python coding skills

  • Numpy, Matplotlib, Scipy, and Pandas (I teach this for free, no excuses!)

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Algorithmic Trading & Time Series Analysis in Python and R

Technical Analysis (SMA and RSI), Time Series Analysis (ARIMA and GRACH), Machine Learning and Mean-Reversion Strategies

Created by Holczer Balazs - Software Engineer

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Students: 3319, Price: $99.99

Students: 3319, Price:  Paid

This course is about the fundamental basics of algorithmic trading. First of all you will learn about stocks, bonds and the fundamental basic of stock market and the FOREX. The main reason of this course is to get a better understanding of mathematical models concerning algorithmic trading and finance in the main.

We will use Python and R as programming languages during the lectures

IMPORTANT: only take this course, if you are interested in statistics and mathematics !!!

Section 1 - Introduction

  • why to use Python as a programming language?

  • installing Python and PyCharm

  • installing R and RStudio

Section 2 - Stock Market Basics

  • types of analyses

  • stocks and shares

  • commodities and the FOREX

  • what are short and long positions?

+++ TECHNICAL ANALYSIS ++++

Section 3 - Moving Average (MA) Indicator

  • simple moving average (SMA) indicators

  • exponential moving average (EMA) indicators

  • the moving average crossover trading strategy

Section 4 - Relative Strength Index (RSI)

  • what is the relative strength index (RSI)?

  • arithmetic returns and logarithmic returns

  • combined moving average and RSI trading strategy

  • Sharpe ratio

Section 5 - Stochastic Momentum Indicator

  • what is stochastic momentum indicator?

  • what is average true range (ATR)?

  • portfolio optimization trading strategy

+++ TIME SERIES ANALYSIS +++

Section 6 - Time Series Fundamentals

  • statistics basics (mean, variance and covariance)

  • downloading data from Yahoo Finance

  • stationarity

  • autocorrelation (serial correlation) and correlogram

Section 7 - Random Walk Model

  • white noise and Gaussian white noise

  • modelling assets with random walk

Section 8 - Autoregressive (AR) Model

  • what is the autoregressive model?

  • how to select best model orders?

  • Akaike information criterion

Section 9 - Moving Average (MA) Model

  • moving average model

  • modelling assets with moving average model

Section 10 - Autoregressive Moving Average Model (ARMA)

  • what is the ARMA and ARIMA models?

  • Ljung-Box test

  • integrated part - I(0) and I(1) processes

Section 11 - Heteroskedastic Processes

  • how to model volatility in finance

  • autoregressive heteroskedastic (ARCH) models

  • generalized autoregressive heteroskedastic (GARCH) models

Section 12 - ARIMA and GARCH Trading Strategy

  • how to combine ARIMA and GARCH model

  • modelling mean and variance

+++ MARKET-NEUTRAL TRADING STRATEGIES +++

Section 13 - Market-Neutral Strategies

  • types of risks (specific and market risk)

  • hedging the market risk (Black-Scholes model and pairs trading)

Section 14 - Mean Reversion

  • Ornstein-Uhlenbeck stochastic processes

  • what is cointegration?

  • pairs trading strategy implementation

  • Bollinger bands and cross-sectional mean reversion

+++ MACHINE LEARNING +++

Section 15 - Logistic Regression

  • what is linear regression

  • when to prefer logistic regression

  • logistic regression trading strategy

Section 16 - Support Vector Machines (SVMs)

  • what are support vector machines?

  • support vector machine trading strategy

  • parameter optimization

APPENDIX - R CRASH COURSE

  • basics - variables, strings, loops and logical operators

  • functions

APPENDIX - PYTHON CRASH COURSE

  • basics - variables, strings, loops and logical operators

  • functions

  • data structures in Python (lists, arrays, tuples and dictionaries)

  • object oriented programming (OOP)

  • NumPy

Thanks for joining my course, let's get started!

Complete Time Series Data Analysis Bootcamp In R

Learn How To Work With Time Series/Temporal Data Using Statistical Modelling & Machine Learning Techniques In R

Created by Minerva Singh - Bestselling Instructor & Data Scientist(Cambridge Uni)

"]

Students: 2339, Price: $99.99

Students: 2339, Price:  Paid

THIS IS YOUR COMPLETE GUIDE TO TIME SERIES DATA ANALYSIS IN R!

This course is your complete guide to time series analysis using R. So, all the main aspects of analyzing temporal data will be covered n depth..

If you take this course, you can do away with taking other courses or buying books on R based data analysis.  

In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in in analyzing time series data in R, you can give your company a competitive edge and boost your career to the next level.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

Hey, my name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University.

I have +5 years of experience in analyzing real life data from different sources  using data science related techniques and i have produced many publications for international peer reviewed journals.

 Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic .

So, unlike other R instructors, I dig deep into the data science features of R and gives you a one-of-a-kind grounding in data science related topics!

You will go all the way from carrying out data reading & cleaning  to to finally implementing powerful statistical and machine learning algorithms for analyzing time series data.

Among other things:

  • You will be introduced to powerful R-based packages for time series analysis.

  • You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for time series data.

  • & you will learn to apply these frameworks to real life data including temporal stocks and financial data.  

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED!

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.

My course will help you implement the methods using REAL DATA obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.

After taking this course, you’ll easily use the common time series packages in R...

You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data.

We will work with real data and you will have access to all the code and data used in the course. 

JOIN MY COURSE NOW!

Python for Time Series Analysis and Forecasting

Work with time series and time related data in Python - Forecasting, Time Series Analysis, Predictive Analytics

Created by R-Tutorials Training - Data Science Education

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Students: 2268, Price: $89.99

Students: 2268, Price:  Paid

Use Python to Understand the Now and Predict the Future!

Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to

  • see patterns in time series data

  • model this data

  • finally make forecasts based on those models

  • and of of this you can now do with the help of Python

Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data collected in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career!

  • What will you learn in this course and how is it structured?

First of all we will discuss the general idea behind time series analysis and forecasting. It is important to know when to use these tools and what they actually do.

After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests.   You will also learn how to read a time series chart. This is a crucial skill because things like mean, variance, trend or seasonality are a determining factor for model selection.

We will also create our own time series charts including smoothers and trend lines.

Then you will see how different models work, how they are set up in Python and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied by homework assignments.

  • Where are those methods applied?

In nearly any field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing  techniques taught in this course are applied.

  • Is it hard to understand and learn those methods?

Unfortunately learning material on Time Series Analysis Programming in Python is quite technical and needs tons of prior knowledge to be understood.

With this course it is the goal to make modeling and forecasting as intuitive and simple as possible for you.

While you need some knowledge in maths and Python, the course is meant for people without a major in a quantitative field. Basically anybody dealing with time data on a regular basis can benefit from this course.

  • How do I prepare best to benefit from this course?

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in Python.

Time Series Analysis, Forecasting, and Machine Learning

Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting

Created by Lazy Programmer Team - Artificial Intelligence and Machine Learning Engineer

"]

Students: 1191, Price: $199.99

Students: 1191, Price:  Paid

Hello friends!

Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python.

Time Series Analysis has become an especially important field in recent years.

  • With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.

  • COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.

  • Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.

Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.

We will cover techniques such as:

  • ETS and Exponential Smoothing

  • Holt's Linear Trend Model

  • Holt-Winters Model

  • ARIMA, SARIMA, SARIMAX, and Auto ARIMA

  • ACF and PACF

  • Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)

  • Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)

  • Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)

  • GRUs and LSTMs for Time Series Forecasting

We will cover applications such as:

  • Time series forecasting of sales data

  • Time series forecasting of stock prices and stock returns

  • Time series classification of smartphone data to predict user behavior

The VIP version of the course will cover even more exciting topics, such as:

  • AWS Forecast (Amazon's state-of-the-art low-code forecasting API)

  • GARCH (financial volatility modeling)

  • FB Prophet (Facebook's time series library)

So what are you waiting for? Signup now to get lifetime access, a certificate of completion you can show off on your LinkedIn profile, and the skills to use the latest time series analysis techniques that you cannot learn anywhere else.

Thanks for reading, and I'll see you in class!

Forecasting Models and Time Series for Business in R 2021

6 forecasting models | Sales & Demand Planning | Time Series Analysis | Prophet | ARIMA | Neural Networks | Ensemble

Created by Diogo Alves de Resende - Econometrics and Data Science enthusiast

"]

Students: 687, Price: $24.99

Students: 687, Price:  Paid

How many times have you wanted to predict the future?

Welcome to the most exciting online course about Forecasting Models and Time Series in R. I will show everything you need to know to understand the now and predict the future.

Forecasting is always sexy - knowing what will happen usually drops jaws and earns admiration. On top, it is fundamental in the business world. Companies always provide Revenue growth and EBIT estimates, which are based on forecasts. Who is doing them? Well, that could be you!

WHY SHOULD YOU ENROLL IN THIS COURSE?

  • YOU WILL LEARN THE INTUITION BEHIND THE TIME SERIES MODELS WITHOUT FOCUSING TOO MUCH ON THE MATH

It is crucial that you know why a model makes sense and the underlying assumptions behind it. I will explain to you each model using words, graphs, and metaphors, leaving math and the Greek alphabet to a minimum.

  • THOROUGH COURSE STRUCTURE OF MOST IMPACTFUL TIME SERIES FORECASTING MODEL TECHNIQUES

The techniques in this course are the ones I believe will be most impactful, up to date, and sought after:

· Holt-Winters

· Sarimax

· TBATS

· Facebook Prophet

· Neural Networks AutoRegression

· Ensemble approach

  • WE CODE TOGETHER LINE BY LINE

I will guide you through every step of the way in your journey to master time series and forecasting models. I will also explain all parameters and functions that you need to use, step by step.

  • YOU APPLY WHAT YOU ARE LEARNING IMMEDIATELY

At the end of each section regarding forecasting techniques, you are shown an exercise to apply what you learn immediately. If you do not manage? Don't worry! We also code together line by line the solutions. The challenges range from predicting the interest in Churrasco (Brazilian BBQ) to the Wikipedia visitors of Peyton Manning.

Did I spike your interest? Join me and learn how to predict the future!

Applied Time Series Analysis in Python

Use Python and Tensorflow to apply the latest statistical and deep learning techniques for time series analysis

Created by Marco Peixeiro - Data Scientist and Instructor

"]

Students: 503, Price: $89.99

Students: 503, Price:  Paid

This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series:

  • stationarity and augmented Dicker-Fuller test

  • seasonality

  • white noise

  • random walk

  • autoregression

  • moving average

  • ACF and PACF,

  • Model selection with AIC (Akaike's Information Criterion)

Then, we move on and apply more complex statistical models for time series forecasting:

  • ARIMA (Autoregressive Integrated Moving Average model)

  • SARIMA (Seasonal Autoregressive Integrated Moving Average model)

  • SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables)

We also cover multiple time series forecasting with:

  • VAR (Vector Autoregression)

  • VARMA (Vector Autoregressive Moving Average model)

  • VARMAX (Vector Autoregressive Moving Average model with exogenous variable)

Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis:

  • Simple linear model (1 layer neural network)

  • DNN (Deep Neural Network)

  • CNN (Convolutional Neural Network)

  • LSTM (Long Short-Term Memory)

  • CNN + LSTM models

  • ResNet (Residual Networks)

  • Autoregressive LSTM

Throughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you.

Time Series Analysis and Forecasting with Python

Learn Python for Pandas, Statsmodels, ARIMA, SARIMAX, Deep Learning, LSTM and Forecasting into Future

Created by Navid Shirzadi - Data Analyst - Optimization Expert

"]

Students: 188, Price: $89.99

Students: 188, Price:  Paid

"Time Series Analysis and Forecasting with Python" Course is an ultimate source for learning the concepts of Time Series and forecast into the future.

In this course, the most famous methods such as statistical methods (ARIMA and SARIMAX) and Deep Learning Method (LSTM) are explained in detail. Furthermore, several Real World projects are developed in a Python environment and have been explained line by line!

If you are a researcher, a student, a programmer, or a data science enthusiast that is seeking a course that shows you all about time series and prediction from A-Z, you are in a right place. Just check out what you will learn in this course below:

  • Basic libraries (NumPy, Pandas, Matplotlib)

  • How to use Pandas library to create DateTime index and how to set that as your Dataset index

  • What are statistical models?

  • How to forecast into future using the ARIMA model?

  • How to capture the seasonality using the SARIMAX model?

  • How to use endogenous variables and predict into future?

  • What is Deep Learning (Very Basic Concepts)

  • All about Artificial and Recurrent Neural Network!

  • How the LSTM method Works!

  • How to develop an LSTM model with a single variate?

  • How to develop an LSTM model using multiple variables (Multivariate)

As I mentioned above, in this course we tried to explain how you can develop an LSTM model when you have several predictors (variables) for the first time and you can use that for several applications and use the source code for your project as well!

This course is for Everyone! yes everyone! that wants t to learn time-series and forecasting into the future using statistics and artificial intelligence with any kind of background! Even if you are not a programmer, I show you how to code and develop your model line by line!

If you want to master the basics of Machine Learning in Python as well, you can check my other courses!

Data Science in Layman’s Terms: Time Series Analysis

Modeling Time Series Data

Created by Nicholas Lincoln - Data Scientist

"]

Students: 41, Price: $29.99

Students: 41, Price:  Paid

This course explores a specific domain of data science: time series analysis.  The lectures explain topics in time series from a high level perspective, so that you can get a logical understanding of the concepts without getting intimidated by the math or programming.  Whether you are new to time series or an experienced data scientist, this course covers every aspect of time series.  Topics in time series analysis include:

  • Forecasting - Predicting the future

  • Classification - Categorize a series

  • Segmentation - Breaking a series into periods of distinct characteristics

  • Anomaly Detection - Identifying unexpected observations

  • Signal Processing - Extracting signal from noise

  • Geospatial-Temporal Analysis - Analyzing time series with a location component

The later half of the course entails several projects for you to get your hands dirty with time series analysis in Python.  You will learn about modern time series forecasting models and AI, how to build them, and implement them to do extraordinary things. 

  • Generate music with AI

  • Deploy a model to an API to provide machine learning as a service (MLaaS)

  • Build a dashboard with Dash/Plotly

  • Build different types of RNNs and Transformers, using TensorFlow, for time series modeling

  • Analyze different types of data sources, like CSV, JSON, GeoJSON, HDF5, and MIDI

By the end of this course, you will be able to handle any time series problem.  You will be equipped with the knowledge to build powerful forecasting models, and be able to deploy them.

Introduction to Time Series Course with Python [2021]

Learn how to use Python , Pandas, Numpy , and Statsmodels for Time Series Forecasting and Analysis!

Created by Hoang Quy La - Electrical Engineer

"]

Students: 20, Price: $89.99

Students: 20, Price:  Paid

Hello everyone!

Welcome to Introduction to Time Series Course with Python [2021].

Time Series Analysis has become an especially important field in recent years.

  • With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.

  • COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.

  • Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.

Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.

We will cover techniques such as:

  • Basic Pandas Operations

  • Advanced Pandas Operations

  • Working with Pandas Datetime

  • Modelling Time Series

  • Components of a Time Series

  • Differencing

  • Percentage Change, Subtracting the mean

  • Correlation in Time Series

  • Rolling Window of Correlations

  • High Correlation

  • AutoCorrelation

  • AR & MA Models

  • ARMA model

  • Decision Tree Model

  • Forest Random Model

  • Gradient Boosted Tree Model

  • Handling Missing Data

  • Cointegration Model

  • Non-Stationary Series and No Cointegration

  • Granger Causality

  • ARIMA Model and forecasting

We will cover applications such as:

  • Netflix dataset

  • Disney dataset

  • Google Trend vacation dataset

  • Spotify dataset

  • Temperature average of ST.Louis dataset

  • Bank of America dataset

  • J.P. Morgan dataset

  • Furniture dataset

  • Corn dataset

So what are you waiting for? Signup now to get lifetime access, a certificate of completion you can show off on your LinkedIn profile, and the skills to use the latest time series analysis techniques that you cannot learn anywhere else.

Thanks for reading, and I'll see you in class!

A Complete Guide to Time Series Analysis & Forecasting in R

A comprehensive time series analysis and forecasting course using R

Created by Dr. Imran Arif - Assistant Professor of Economics

"]

Students: 11, Price: $19.99

Students: 11, Price:  Paid

Forecasting involves making predictions. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments) or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an essential aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

  • No prior knowledge of R or data science is required.

  • Emphasis on applications of time-series analysis and forecasting rather than theory and mathematical derivations.

  • Plenty of rigorous examples and quizzes for an extensive learning experience.

  • All course contents are self-explanatory.

  • All R codes and data sets and provided for replication and practice.

At the completion of this course, you will be able to

  • Explore and visualize time series data.

  • Apply and interpret time series regression results.

  • Understand various methods to forecast time series data.

  • Use general forecasting tools and models for different forecasting situations.

  • Utilize statistical programs to compute, visualize, and analyze time-series data in economics, business, and the social sciences.

You will learn

  • Exploring and visualizing time series in R.

  • Benchmark methods of time series forecasting.

  • Time series forecasting forecast accuracy.

  • Linear regression models.

  • Exponential smoothing.

  • Stationarity, ADF, KPSS, differencing, etc.

  • ARIMA, SARIMA, and ARIMAX (dynamic regression) models.

  • Other forecasting models.