Mô tả

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!


UNIQUE FEATURES

  • Every line of code explained in detail - email me any time if you disagree

  • No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch

  • Not afraid of university-level math - get important details about algorithms that other courses leave out

Bạn sẽ học được gì

ETS and Exponential Smoothing Models

Holt's Linear Trend Model and Holt-Winters

Autoregressive and Moving Average Models (ARIMA)

Seasonal ARIMA (SARIMA), and SARIMAX

Auto ARIMA

The statsmodels Python library

The pmdarima Python library

Machine learning for time series forecasting

Deep learning (ANNs, CNNs, RNNs, and LSTMs) for time series forecasting

Tensorflow 2 for predicting stock prices and returns

Vector autoregression (VAR) and vector moving average (VMA) models (VARMA)

AWS Forecast (Amazon's time series forecasting service)

FB Prophet (Facebook's time series library)

Modeling and forecasting financial time series

GARCH (volatility modeling)

Yêu cầu

  • Decent Python coding skills
  • Numpy, Matplotlib, Pandas, and Scipy (I teach this for free! My gift to the community)
  • Matrix arithmetic
  • Probability

Nội dung khoá học

17 sections

Welcome

2 lectures
Introduction and Outline
05:23
Warmup (Optional)
04:46

Getting Set Up

3 lectures
Where to get the code, notebooks, and data
04:29
How to Succeed in This Course
03:04
Temporary 403 Errors
02:57

Time Series Basics

15 lectures
Time Series Basics Section Introduction
04:30
What is a Time Series?
04:39
Modeling vs. Predicting
02:28
Why Do We Care About Shapes?
06:13
Types of Tasks
06:33
Power, Log, and Box-Cox Transformations
06:03
Power, Log, and Box-Cox Transformations in Code
06:05
Forecasting Metrics
11:22
Financial Time Series Primer
11:02
Price Simulations in Code
03:06
Random Walks and the Random Walk Hypothesis
14:35
The Naive Forecast and the Importance of Baselines
06:45
Naive Forecast and Forecasting Metrics in Code
07:14
Time Series Basics Section Summary
03:14
Suggestion Box
03:10

Exponential Smoothing and ETS Methods

20 lectures
Exponential Smoothing Section Introduction
03:02
Exponential Smoothing Intuition for Beginners
05:37
SMA Theory
03:36
SMA Code
08:41
EWMA Theory
11:07
EWMA Code
07:39
SES Theory
10:13
SES Code
11:55
Holt's Linear Trend Model (Theory)
07:55
Holt's Linear Trend Model (Code)
03:13
Holt-Winters (Theory)
11:20
Holt-Winters (Code)
07:52
Walk-Forward Validation
09:06
Walk-Forward Validation in Code
08:29
Application: Sales Data
05:00
Application: Stock Predictions
05:37
SMA Application: COVID-19 Counting
03:06
SMA Application: Algorithmic Trading
02:08
Exponential Smoothing Section Summary
03:59
(Optional) More About State-Space Models
11:22

ARIMA

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

Vector Autoregression (VAR, VMA, VARMA)

11 lectures
Vector Autoregression Section Introduction
02:30
VAR and VARMA Theory
13:11
VARMA Code (pt 1)
07:36
VARMA Code (pt 2)
06:47
VARMA Code (pt 3)
06:25
VARMA Econometrics Code (pt 1)
07:51
VARMA Econometrics Code (pt 2)
09:17
Granger Causality
04:28
Granger Causality Code
03:19
Converting Between Models (Optional)
11:45
Vector Autoregression Section Summary
03:39

Machine Learning Methods

15 lectures
Machine Learning Section Introduction
03:52
Supervised Machine Learning: Classification and Regression
14:26
Autoregressive Machine Learning Models
07:34
Machine Learning Algorithms: Linear Regression
05:05
Machine Learning Algorithms: Logistic Regression
06:54
Machine Learning Algorithms: Support Vector Machines
10:02
Machine Learning Algorithms: Random Forest
06:52
Extrapolation and Stock Prices
08:47
Machine Learning for Time Series Forecasting in Code (pt 1)
13:00
Forecasting with Differencing
04:21
Machine Learning for Time Series Forecasting in Code (pt 2)
06:47
Application: Sales Data
05:24
Application: Predicting Stock Prices and Returns
04:52
Application: Predicting Stock Movements
04:06
Machine Learning Section Summary
02:23

Deep Learning: Artificial Neural Networks (ANN)

17 lectures
Artificial Neural Networks: Section Introduction
03:24
The Neuron
09:58
Forward Propagation
09:40
The Geometrical Picture
09:43
Activation Functions
17:18
Multiclass Classification
08:41
ANN Code Preparation
11:56
Feedforward ANN for Time Series Forecasting Code
10:15
Feedforward ANN for Stock Return and Price Predictions Code
08:50
Human Activity Recognition Dataset
05:53
Human Activity Recognition: Code Preparation
06:23
Human Activity Recognition: Data Exploration
07:35
Human Activity Recognition: Multi-Input ANN
10:59
Human Activity Recognition: Feature-Based Model
05:56
Human Activity Recognition: Combined Model
03:06
How Does a Neural Network "Learn"?
10:49
Artificial Neural Networks: Section Summary
02:18

Deep Learning: Convolutional Neural Networks (CNN)

11 lectures
CNN Section Introduction
03:07
What is Convolution?
16:38
What is Convolution? (Pattern-Matching)
05:56
What is Convolution? (Weight Sharing)
06:41
Convolution on Color Images
15:58
Convolution for Time Series and ARIMA
04:59
CNN Architecture
23:21
CNN Code Preparation
06:16
CNN for Time Series Forecasting in Code
06:45
CNN for Human Activity Recognition
06:22
CNN Section Summary
03:14

Deep Learning: Recurrent Neural Networks (RNN)

12 lectures
RNN Section Introduction
04:46
Simple RNN / Elman Unit (pt 1)
09:20
Simple RNN / Elman Unit (pt 2)
09:42
Aside: State Space Models vs. RNNs
03:30
RNN Code Preparation
08:38
RNNs: Understanding by Implementing (Paying Attention to Shapes)
08:26
GRU and LSTM (pt 1)
17:35
GRU and LSTM (pt 2)
11:36
LSTMs for Time Series Forecasting in Code
28:59
LSTMs for Time Series Classification in Code
06:10
The Unreasonable Ineffectiveness of Recurrent Neural Networks
03:18
RNN Section Summary
02:57

VIP: GARCH

14 lectures
GARCH Section Introduction
03:56
ARCH Theory (pt 1)
04:57
ARCH Theory (pt 2)
07:36
ARCH Theory (pt 3)
05:15
GARCH Theory
07:40
GARCH Code Preparation (pt 1)
07:54
GARCH Code Preparation (pt 2)
07:55
GARCH Code (pt 1)
06:07
GARCH Code (pt 2)
08:30
GARCH Code (pt 3)
07:11
GARCH Code (pt 4)
05:52
GARCH Code (pt 5)
04:20
A Deep Learning Approach to GARCH
11:27
GARCH Section Summary
06:36

VIP: AWS Forecast

9 lectures
AWS Forecast Section Introduction
08:02
Data Model
09:16
Creating an IAM Role
04:09
Code pt 1 (Getting and Transforming the Data)
09:59
Code pt 2 (Uploading the data to S3)
12:52
Code pt 3 (Building your Model)
06:52
Code pt 4 (Generating and Evaluating the Forecast)
06:49
AWS Forecast Exercise
02:54
AWS Forecast Section Summary
04:55

VIP: Facebook Prophet

11 lectures
Prophet Section Introduction
03:11
How does Prophet work?
08:24
Prophet: Code Preparation
12:41
Prophet in Code: Data Preparation
08:59
Prophet in Code: Fit, Forecast, Plot
08:30
Prophet in Code: Holidays and Exogenous Regressors
10:19
Prophet in Code: Cross-Validation
06:07
Prophet in Code: Changepoint Detection
04:14
Prophet: Multiplicative Seasonality, Outliers, Non-Daily Data
10:16
(The Dangers of) Prophet for Stock Price Prediction
13:10
Prophet Section Summary
03:27

Setting Up Your Environment FAQ

3 lectures
Pre-Installation Check
04:12
Anaconda Environment Setup
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:22

Extra Help With Python Coding for Beginners FAQ

5 lectures
How to Code by Yourself (part 1)
15:56
How to Code by Yourself (part 2)
09:23
Proof that using Jupyter Notebook is the same as not using it
12:29
Get Your Hands Dirty, Practical Coding Experience, Data Links
08:33
How to use Github & Extra Coding Tips (Optional)
11:12

Effective Learning Strategies for Machine Learning FAQ

4 lectures
How to Succeed in this Course (Long Version)
10:24
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
Machine Learning and AI Prerequisite Roadmap (pt 1)
11:18
Machine Learning and AI Prerequisite Roadmap (pt 2)
16:07

Appendix / FAQ Finale

2 lectures
What is the Appendix?
02:48
BONUS
05:48

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