Time Series Analysis, Forecasting, and Machine Learning

Time Series Analysis, Forecasting, and Machine Learning

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 170 lectures (22h 44m) | 6.82 GB

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

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
  • 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)

What you’ll learn

  • 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)
Table of Contents

1 Introduction and Outline
2 Warmup (Optional)

Getting Set Up
3 Where to Get the Code
4 How to use Github & Extra Coding Tips (Optional)

Time Series Basics
5 Time Series Basics Section Introduction
6 What is a Time Series
7 Modeling vs. Predicting
8 Why Do We Care About Shapes
9 Types of Tasks
10 Power, Log, and Box-Cox Transformations
11 Power, Log, and Box-Cox Transformations in Code
12 Forecasting Metrics
13 Financial Time Series Primer
14 Price Simulations in Code
15 Random Walks and the Random Walk Hypothesis
16 The Naive Forecast and the Importance of Baselines
17 Naive Forecast and Forecasting Metrics in Code
18 Time Series Basics Section Summary
19 Suggestion Box

Exponential Smoothing and ETS Methods
20 Exponential Smoothing Section Introduction
21 Exponential Smoothing Intuition for Beginners
22 SMA Theory
23 SMA Code
24 EWMA Theory
25 EWMA Code
26 SES Theory
27 SES Code
28 Holt’s Linear Trend Model (Theory)
29 Holt’s Linear Trend Model (Code)
30 Holt-Winters (Theory)
31 Holt-Winters (Code)
32 Walk-Forward Validation
33 Walk-Forward Validation in Code
34 Application Sales Data
35 Application Stock Predictions
36 SMA Application COVID-19 Counting
37 SMA Application Algorithmic Trading
38 Exponential Smoothing Section Summary
39 (Optional) More About State-Space Models

40 ARIMA Section Introduction
41 Autoregressive Models – AR(p)
42 Moving Average Models – MA(q)
44 ARIMA in Code
45 Stationarity
46 Stationarity in Code
47 ACF (Autocorrelation Function)
48 PACF (Partial Autocorrelation Funtion)
49 ACF and PACF in Code (pt 1)
50 ACF and PACF in Code (pt 2)
52 Model Selection, AIC and BIC
53 Auto ARIMA in Code
54 Auto ARIMA in Code (Stocks)
55 ACF and PACF for Stock Returns
56 Auto ARIMA in Code (Sales Data)
57 How to Forecast with ARIMA
58 Forecasting Out-Of-Sample
59 ARIMA Section Summary

Vector Autoregression (VAR, VMA, VARMA)
60 Vector Autoregression Section Introduction
61 VAR and VARMA Theory
62 VARMA Code (pt 1)
63 VARMA Code (pt 2)
64 VARMA Code (pt 3)
65 VARMA Econometrics Code (pt 1)
66 VARMA Econometrics Code (pt 2)
67 Granger Causality
68 Granger Causality Code
69 Converting Between Models (Optional)
70 Vector Autoregression Section Summary

Machine Learning Methods
71 Machine Learning Section Introduction
72 Supervised Machine Learning Classification and Regression
73 Autoregressive Machine Learning Models
74 Machine Learning Algorithms Linear Regression
75 Machine Learning Algorithms Logistic Regression
76 Machine Learning Algorithms Support Vector Machines
77 Machine Learning Algorithms Random Forest
78 Extrapolation and Stock Prices
79 Machine Learning for Time Series Forecasting in Code (pt 1)
80 Forecasting with Differencing
81 Machine Learning for Time Series Forecasting in Code (pt 2)
82 Application Sales Data
83 Application Predicting Stock Prices and Returns
84 Application Predicting Stock Movements
85 Machine Learning Section Summary

Deep Learning Artificial Neural Networks (ANN)
86 Human Activity Recognition Combined Model
87 How Does a Neural Network Learn
88 Artificial Neural Networks Section Summary
89 Artificial Neural Networks Section Introduction
90 The Neuron
91 Forward Propagation
92 The Geometrical Picture
93 Activation Functions
94 Multiclass Classification
95 ANN Code Preparation
96 Feedforward ANN for Time Series Forecasting Code
97 Feedforward ANN for Stock Return and Price Predictions Code
98 Human Activity Recognition Dataset
99 Human Activity Recognition Code Preparation
100 Human Activity Recognition Data Exploration
101 Human Activity Recognition Multi-Input ANN
102 Human Activity Recognition Feature-Based Model

Deep Learning Convolutional Neural Networks (CNN)
103 CNN Section Introduction
104 What is Convolution
105 What is Convolution (Pattern-Matching)
106 What is Convolution (Weight Sharing)
107 Convolution on Color Images
108 Convolution for Time Series and ARIMA
109 CNN Architecture
110 CNN Code Preparation
111 CNN for Time Series Forecasting in Code
112 CNN for Human Activity Recognition
113 CNN Section Summary

Deep Learning Recurrent Neural Networks (RNN)
114 RNN Section Introduction
115 Simple RNN Elman Unit (pt 1)
116 Simple RNN Elman Unit (pt 2)
117 Aside State Space Models vs. RNNs
118 RNN Code Preparation
119 RNNs Understanding by Implementing (Paying Attention to Shapes)
120 GRU and LSTM (pt 1)
121 GRU and LSTM (pt 2)
122 LSTMs for Time Series Forecasting in Code
123 LSTMs for Time Series Classification in Code
124 The Unreasonable Ineffectiveness of Recurrent Neural Networks
125 RNN Section Summary

126 GARCH Section Introduction
127 ARCH Theory (pt 1)
128 ARCH Theory (pt 2)
129 ARCH Theory (pt 3)
130 GARCH Theory
131 GARCH Code Preparation (pt 1)
132 GARCH Code Preparation (pt 2)
133 GARCH Code (pt 1)
134 GARCH Code (pt 2)
135 GARCH Code (pt 3)
136 GARCH Code (pt 4)
137 GARCH Code (pt 5)
138 A Deep Learning Approach to GARCH
139 GARCH Section Summary

VIP AWS Forecast
140 AWS Forecast Section Introduction
141 Data Model
142 Creating an IAM Role
143 Code pt 1 (Getting and Transforming the Data)
144 Code pt 2 (Uploading the data to S3)
145 Code pt 3 (Building your Model)
146 Code pt 4 (Generating and Evaluating the Forecast)
147 AWS Forecast Exercise
148 AWS Forecast Section Summary

VIP Facebook Prophet
149 Prophet Section Introduction
150 How does Prophet work
151 Prophet Code Preparation
152 Prophet in Code Data Preparation
153 Prophet in Code Fit, Forecast, Plot
154 Prophet in Code Holidays and Exogenous Regressors
155 Prophet in Code Cross-Validation
156 Prophet in Code Changepoint Detection
157 Prophet Multiplicative Seasonality, Outliers, Non-Daily Data
158 (The Dangers of) Prophet for Stock Price Prediction
159 Prophet Section Summary

Setting Up Your Environment FAQ
160 Anaconda Environment Setup
161 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Extra Help With Python Coding for Beginners FAQ
162 How to Code by Yourself (part 1)
163 How to Code by Yourself (part 2)
164 Proof that using Jupyter Notebook is the same as not using it

Effective Learning Strategies for Machine Learning FAQ
165 How to Succeed in this Course (Long Version)
166 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
167 Machine Learning and AI Prerequisite Roadmap (pt 1)
168 Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix FAQ Finale
169 What is the Appendix
170 BONUS Lecture