Time Series Analysis, Forecasting, and Machine Learning in Python

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

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Course Data

Lectures: 173
Length: 23h 13m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

Course Description

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)


Thanks for reading, and I'll 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!)

Testimonials and Success Stories


I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...

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I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

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By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.

Lectures

Welcome

2 Lectures · 10min

Getting Set Up

2 Lectures · 12min
  1. Where to get the code and data - instant access (01:42)
  2. How to use Github & Extra Coding Tips (Optional) (11:12)

Time Series Basics

15 Lectures · 01hr 40min
  1. Time Series Basics Section Introduction (04:18)
  2. What is a Time Series? (04:52)
  3. Modeling vs. Predicting (02:30)
  4. Why Do We Care About Shapes? (05:56)
  5. Types of Tasks (06:33)
  6. Power, Log, and Box-Cox Transformations (06:03)
  7. Power, Log, and Box-Cox Transformations in Code (06:05)
  8. Forecasting Metrics (11:22)
  9. Financial Time Series Primer (11:02)
  10. Price Simulations in Code (03:06)
  11. Random Walks and the Random Walk Hypothesis (14:35)
  12. The Naive Forecast and the Importance of Baselines (06:45)
  13. Naive Forecast and Forecasting Metrics in Code (07:14)
  14. Time Series Basics Section Summary (07:14)
  15. Suggestion Box (03:10)

Exponential Smoothing and ETS Methods

20 Lectures · 02hr 20min
  1. Exponential Smoothing Section Introduction (03:02)
  2. Exponential Smoothing Intuition for Beginners (05:37)
  3. SMA Theory (03:36)
  4. SMA Code (08:41)
  5. EWMA Theory (11:07)
  6. EWMA Code (07:39)
  7. SES Theory (10:13)
  8. SES Code (11:55)
  9. Holt's Linear Trend Model (Theory) (07:55)
  10. Holt's Linear Trend Model (Code) (03:13)
  11. Holt-Winters (Theory) (11:20)
  12. Holt-Winters (Code) (07:52)
  13. Walk-Forward Validation (09:06)
  14. Walk-Forward Validation in Code (08:29)
  15. Application: Sales Data (05:00)
  16. Application: Stock Predictions (05:37)
  17. SMA Application: COVID-19 Counting (03:06)
  18. SMA Application: Algorithmic Trading (02:08)
  19. Exponential Smoothing Section Summary (03:59)
  20. (Optional) More About State-Space Models (11:22)

ARIMA

20 Lectures · 03hr 07min
  1. ARIMA Section Introduction (05:18)
  2. Autoregressive Models - AR(p) (12:51)
  3. Moving Average Models - MA(q) (03:31)
  4. ARIMA (10:45)
  5. ARIMA in Code (19:15)
  6. Stationarity (13:01)
  7. Stationarity in Code (09:50)
  8. ACF (Autocorrelation Function) (10:10)
  9. PACF (Partial Autocorrelation Funtion) (06:55)
  10. ACF and PACF in Code (pt 1) (08:26)
  11. ACF and PACF in Code (pt 2) (07:03)
  12. Auto ARIMA and SARIMAX (09:41)
  13. Model Selection, AIC and BIC (09:50)
  14. Auto ARIMA in Code (14:04)
  15. Auto ARIMA in Code (Stocks) (15:45)
  16. ACF and PACF for Stock Returns (07:01)
  17. Auto ARIMA in Code (Sales Data) (09:45)
  18. How to Forecast with ARIMA (09:14)
  19. Forecasting Out-Of-Sample (01:26)
  20. ARIMA Section Summary (03:31)

Vector Autoregression (VAR, VMA, VARMA)

11 Lectures · 01hr 16min
  1. Vector Autoregression Section Introduction (02:30)
  2. VAR and VARMA Theory (13:11)
  3. VARMA Code (pt 1) (07:36)
  4. VARMA Code (pt 2) (06:47)
  5. VARMA Code (pt 3) (06:25)
  6. VARMA Econometrics Code (pt 1) (07:51)
  7. VARMA Econometrics Code (pt 2) (09:17)
  8. Granger Causality (04:28)
  9. Granger Causality in Code (03:19)
  10. Converting Between Models (Optional) (11:45)
  11. Vector Autoregression Section Summary (03:39)

Machine Learning Methods

15 Lectures · 01hr 44min
  1. Machine Learning Section Introduction (03:52)
  2. Supervised Machine Learning: Classification and Regression (14:26)
  3. Autoregressive Machine Learning Models (07:34)
  4. Machine Learning Algorithms: Linear Regression (05:05)
  5. Machine Learning Algorithms: Logistic Regression (06:54)
  6. Machine Learning Algorithms: Support Vector Machines (10:02)
  7. Machine Learning Algorithms: Random Forest (06:52)
  8. Extrapolation and Stock Prices (08:47)
  9. Machine Learning for Time Series Forecasting in Code (pt 1) (13:00)
  10. Forecasting with Differencing (04:21)
  11. Machine Learning for Time Series Forecasting in Code (pt 2) (06:47)
  12. Application: Sales Data (05:24)
  13. Application: Predicting Stock Prices and Returns (04:52)
  14. Application: Predicting Stock Movements (04:06)
  15. Machine Learning Section Summary (02:23)

Deep Learning: Artificial Neural Networks (ANN)

17 Lectures · 02hr 22min
  1. Artificial Neural Networks: Section Introduction (03:24)
  2. The Neuron (09:58)
  3. Forward Propagation (09:40)
  4. The Geometrical Picture (09:43)
  5. Activation Functions (17:18)
  6. Multiclass Classification (08:41)
  7. ANN Code Preparation (11:56)
  8. Feedforward ANN for Time Series Forecasting Code (10:15)
  9. Feedforward ANN for Stock Return and Price Predictions Code (08:50)
  10. Human Activity Recognition Dataset (05:53)
  11. Human Activity Recognition: Code Preparation (06:23)
  12. Human Activity Recognition: Data Exploration (07:35)
  13. Human Activity Recognition: Multi-Input ANN (10:59)
  14. Human Activity Recognition: Feature-Based Model (05:56)
  15. Human Activity Recognition: Combined Model (03:06)
  16. How Does a Neural Network "Learn"? (10:49)
  17. Artificial Neural Networks: Section Summary (02:18)

Deep Learning: Convolutional Neural Networks (CNN)

11 Lectures · 01hr 39min
  1. CNN Section Introduction (03:07)
  2. What is Convolution? (16:38)
  3. What is Convolution? (Pattern-Matching) (05:56)
  4. What is Convolution? (Weight Sharing) (06:41)
  5. Convolution on Color Images (15:58)
  6. Convolution for Time Series and ARIMA (04:59)
  7. CNN Architecture (23:21)
  8. CNN Code Preparation (06:16)
  9. CNN for Time Series Forecasting in Code (06:45)
  10. CNN for Human Activity Recognition (06:22)
  11. CNN Section Summary (03:14)

Deep Learning: Recurrent Neural Networks (RNN)

12 Lectures · 01hr 35min
  1. RNN Section Introduction (04:46)
  2. Simple RNN / Elman Unit (pt 1) (09:20)
  3. Simple RNN / Elman Unit (pt 2) (09:42)
  4. Aside: State Space Models vs. RNNs (03:30)
  5. RNN Code Preparation (08:38)
  6. RNNs: Understanding by Implementing (Paying Attention to Shapes) (08:26)
  7. GRU and LSTM (pt 1) (17:35)
  8. GRU and LSTM (pt 2) (11:36)
  9. LSTMs for Time Series Forecasting in Code (09:28)
  10. LSTMs for Time Series Classification in Code (06:10)
  11. The Unreasonable Ineffectiveness of Recurrent Neural Networks (03:18)
  12. RNN Section Summary (02:57)

VIP: GARCH

14 Lectures · 01hr 35min
  1. GARCH Section Introduction (03:56)
  2. ARCH Theory (pt 1) (04:57)
  3. ARCH Theory (pt 2) (07:36)
  4. ARCH Theory (pt 3) (05:14)
  5. GARCH Theory (07:40)
  6. GARCH Code Preparation (pt 1) (07:54)
  7. GARCH Code Preparation (pt 2) (07:55)
  8. GARCH Code (pt 1) (06:07)
  9. GARCH Code (pt 2) (08:30)
  10. GARCH Code (pt 3) (07:11)
  11. GARCH Code (pt 4) (05:52)
  12. GARCH Code (pt 5) (04:20)
  13. A Deep Learning Approach to GARCH (11:27)
  14. GARCH Section Summary (06:36)

VIP: AWS Forecast

9 Lectures · 01hr 05min
  1. AWS Forecast Section Introduction (08:02)
  2. Data Model (09:16)
  3. Creating an IAM Role (04:09)
  4. Code pt 1 (Getting and Transforming the Data) (09:59)
  5. Code pt 2 (Uploading the data to S3) (12:52)
  6. Code pt 3 (Building your Model) (06:52)
  7. Code pt 4 (Generating and Evaluating the Forecast) (06:49)
  8. AWS Forecast Exercise (02:54)
  9. AWS Forecast Section Summary (04:55)

VIP: Facebook Prophet

11 Lectures · 01hr 29min
  1. Prophet Section Introduction (03:11)
  2. How does Prophet work? (08:24)
  3. Prophet: Code Preparation (12:41)
  4. Prophet in Code: Data Preparation (08:59)
  5. Prophet in Code: Fit, Forecast, Plot (08:30)
  6. Prophet in Code: Holidays and Exogenous Regressors (10:19)
  7. Prophet in Code: Cross-Validation (06:07)
  8. Prophet in Code: Changepoint Detection (04:14)
  9. Prophet: Multiplicative Seasonality, Outliers, Non-Daily Data (10:16)
  10. (The Dangers of) Prophet for Stock Price Prediction (13:10)
  11. Prophet Section Summary (03:27)

Setting Up Your Environment (Appendix/FAQ by Student Request)

2 Lectures · 37min
  1. Windows-Focused Environment Setup 2018 (20:21)
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

6 Lectures · 01hr 05min
  1. Beginner's Coding Tips (13:22)
  2. How to Code Yourself (part 1) (15:55)
  3. How to Code Yourself (part 2) (09:24)
  4. Proof that using Jupyter Notebook is the same as not using it (12:29)
  5. Python 2 vs Python 3 (04:38)
  6. Is Theano Dead? (10:04)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)
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