Time Series Analysis, Forecasting, and Machine Learning

22h 47m 45s
English
Paid
December 2, 2024

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.

More

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.

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)

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# Title Duration
1 Introduction and Outline 05:26
2 Warmup (Optional) 04:34
3 Where to Get the Code 08:00
4 How to use Github & Extra Coding Tips (Optional) 08:57
5 Time Series Basics Section Introduction 04:19
6 What is a Time Series? 04:53
7 Modeling vs. Predicting 02:31
8 Why Do We Care About Shapes? 05:57
9 Types of Tasks 06:34
10 Power, Log, and Box-Cox Transformations 06:04
11 Power, Log, and Box-Cox Transformations in Code 06:06
12 Forecasting Metrics 11:23
13 Financial Time Series Primer 11:03
14 Price Simulations in Code 03:07
15 Random Walks and the Random Walk Hypothesis 14:36
16 The Naive Forecast and the Importance of Baselines 06:46
17 Naive Forecast and Forecasting Metrics in Code 07:15
18 Time Series Basics Section Summary 03:15
19 Suggestion Box 03:11
20 Exponential Smoothing Section Introduction 03:03
21 Exponential Smoothing Intuition for Beginners 05:38
22 SMA Theory 03:37
23 SMA Code 08:42
24 EWMA Theory 11:08
25 EWMA Code 07:40
26 SES Theory 10:14
27 SES Code 11:56
28 Holt's Linear Trend Model (Theory) 07:56
29 Holt's Linear Trend Model (Code) 03:14
30 Holt-Winters (Theory) 11:21
31 Holt-Winters (Code) 07:53
32 Walk-Forward Validation 09:07
33 Walk-Forward Validation in Code 08:30
34 Application: Sales Data 05:01
35 Application: Stock Predictions 05:38
36 SMA Application: COVID-19 Counting 03:07
37 SMA Application: Algorithmic Trading 02:09
38 Exponential Smoothing Section Summary 04:00
39 (Optional) More About State-Space Models 11:23
40 ARIMA Section Introduction 05:19
41 Autoregressive Models - AR(p) 12:52
42 Moving Average Models - MA(q) 03:32
43 ARIMA 10:46
44 ARIMA in Code 19:16
45 Stationarity 13:02
46 Stationarity in Code 09:51
47 ACF (Autocorrelation Function) 10:11
48 PACF (Partial Autocorrelation Funtion) 06:56
49 ACF and PACF in Code (pt 1) 08:27
50 ACF and PACF in Code (pt 2) 07:04
51 Auto ARIMA and SARIMAX 09:42
52 Model Selection, AIC and BIC 09:51
53 Auto ARIMA in Code 14:05
54 Auto ARIMA in Code (Stocks) 15:46
55 ACF and PACF for Stock Returns 07:02
56 Auto ARIMA in Code (Sales Data) 09:46
57 How to Forecast with ARIMA 09:15
58 Forecasting Out-Of-Sample 01:27
59 ARIMA Section Summary 03:32
60 Vector Autoregression Section Introduction 02:31
61 VAR and VARMA Theory 13:12
62 VARMA Code (pt 1) 07:37
63 VARMA Code (pt 2) 06:48
64 VARMA Code (pt 3) 06:26
65 VARMA Econometrics Code (pt 1) 07:52
66 VARMA Econometrics Code (pt 2) 09:18
67 Granger Causality 04:29
68 Granger Causality Code 03:20
69 Converting Between Models (Optional) 11:46
70 Vector Autoregression Section Summary 03:40
71 Machine Learning Section Introduction 03:53
72 Supervised Machine Learning: Classification and Regression 14:27
73 Autoregressive Machine Learning Models 07:35
74 Machine Learning Algorithms: Linear Regression 05:06
75 Machine Learning Algorithms: Logistic Regression 06:55
76 Machine Learning Algorithms: Support Vector Machines 10:03
77 Machine Learning Algorithms: Random Forest 06:53
78 Extrapolation and Stock Prices 08:48
79 Machine Learning for Time Series Forecasting in Code (pt 1) 13:01
80 Forecasting with Differencing 04:22
81 Machine Learning for Time Series Forecasting in Code (pt 2) 06:48
82 Application: Sales Data 05:25
83 Application: Predicting Stock Prices and Returns 04:53
84 Application: Predicting Stock Movements 04:07
85 Machine Learning Section Summary 02:24
86 Artificial Neural Networks: Section Introduction 03:25
87 The Neuron 09:59
88 Forward Propagation 09:41
89 The Geometrical Picture 09:44
90 Activation Functions 17:19
91 Multiclass Classification 08:42
92 ANN Code Preparation 11:57
93 Feedforward ANN for Time Series Forecasting Code 10:16
94 Feedforward ANN for Stock Return and Price Predictions Code 08:51
95 Human Activity Recognition Dataset 05:54
96 Human Activity Recognition: Code Preparation 06:24
97 Human Activity Recognition: Data Exploration 07:36
98 Human Activity Recognition: Multi-Input ANN 11:00
99 Human Activity Recognition: Feature-Based Model 05:57
100 Human Activity Recognition: Combined Model 03:07
101 How Does a Neural Network "Learn"? 10:50
102 Artificial Neural Networks: Section Summary 02:19
103 CNN Section Introduction 03:08
104 What is Convolution? 16:39
105 What is Convolution? (Pattern-Matching) 05:57
106 What is Convolution? (Weight Sharing) 06:56
107 Convolution on Color Images 15:59
108 Convolution for Time Series and ARIMA 05:00
109 CNN Architecture 23:22
110 CNN Code Preparation 06:17
111 CNN for Time Series Forecasting in Code 06:46
112 CNN for Human Activity Recognition 06:23
113 CNN Section Summary 03:15
114 RNN Section Introduction 04:47
115 Simple RNN / Elman Unit (pt 1) 09:21
116 Simple RNN / Elman Unit (pt 2) 09:43
117 Aside: State Space Models vs. RNNs 03:31
118 RNN Code Preparation 08:39
119 RNNs: Understanding by Implementing (Paying Attention to Shapes) 08:27
120 GRU and LSTM (pt 1) 17:36
121 GRU and LSTM (pt 2) 11:37
122 LSTMs for Time Series Forecasting in Code 09:29
123 LSTMs for Time Series Classification in Code 06:11
124 The Unreasonable Ineffectiveness of Recurrent Neural Networks 03:19
125 RNN Section Summary 02:58
126 GARCH Section Introduction 03:57
127 ARCH Theory (pt 1) 04:58
128 ARCH Theory (pt 2) 07:37
129 ARCH Theory (pt 3) 05:16
130 GARCH Theory 07:41
131 GARCH Code Preparation (pt 1) 07:55
132 GARCH Code Preparation (pt 2) 07:56
133 GARCH Code (pt 1) 06:08
134 GARCH Code (pt 2) 08:31
135 GARCH Code (pt 3) 07:12
136 GARCH Code (pt 4) 05:53
137 GARCH Code (pt 5) 04:21
138 A Deep Learning Approach to GARCH 11:28
139 GARCH Section Summary 06:37
140 AWS Forecast Section Introduction 08:03
141 Data Model 09:17
142 Creating an IAM Role 04:10
143 Code pt 1 (Getting and Transforming the Data) 10:00
144 Code pt 2 (Uploading the data to S3) 12:53
145 Code pt 3 (Building your Model) 06:53
146 Code pt 4 (Generating and Evaluating the Forecast) 06:50
147 AWS Forecast Exercise 02:55
148 AWS Forecast Section Summary 04:56
149 Prophet Section Introduction 03:12
150 How does Prophet work? 08:25
151 Prophet: Code Preparation 12:42
152 Prophet in Code: Data Preparation 09:00
153 Prophet in Code: Fit, Forecast, Plot 08:31
154 Prophet in Code: Holidays and Exogenous Regressors 10:20
155 Prophet in Code: Cross-Validation 06:08
156 Prophet in Code: Changepoint Detection 04:15
157 Prophet: Multiplicative Seasonality, Outliers, Non-Daily Data 10:17
158 (The Dangers of) Prophet for Stock Price Prediction 13:11
159 Prophet Section Summary 03:28
160 Anaconda Environment Setup 20:21
161 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:23
162 How to Code by Yourself (part 1) 15:57
163 How to Code by Yourself (part 2) 09:24
164 Proof that using Jupyter Notebook is the same as not using it 12:30
165 How to Succeed in this Course (Long Version) 10:25
166 Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? 22:05
167 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:19
168 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:08
169 What is the Appendix? 02:49
170 BONUS Lecture 05:32

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