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
- 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)

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

**Welcome**

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

**ARIMA**

40 ARIMA Section Introduction

41 Autoregressive Models – AR(p)

42 Moving Average Models – MA(q)

43 ARIMA

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)

51 Auto ARIMA and SARIMAX

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

**VIP GARCH**

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

Resolve the captcha to access the links!