CourseFlix

Time Series Analysis, Forecasting, and Machine Learning

22h 47m 45s
English
Paid

Unveiling a new era in time series analysis, this course is designed to transcend traditional methods and immerse you in revolutionary advancements like deep learning and time series classification. From interpreting smartphone data for user insights to decoding brain activity, this course offers an expansive skill set.

Importance of Time Series Analysis

Time Series Analysis has gained tremendous significance in the modern world due to various reasons:

  • The rising inflation has led individuals to explore the stock market and cryptocurrencies to maintain the value of their savings.

  • The COVID-19 pandemic underscored the critical role of forecasting in guiding public health decisions.

  • Businesses are leveraging forecasts to enhance efficiency in inventory management and operational planning.

Covered Techniques

Participants will master a range of techniques, including:

  • ETS and Exponential Smoothing

  • Holt’s Linear Trend Model and Holt-Winters Model

  • ARIMA, SARIMA, SARIMAX, and Auto ARIMA

  • Analysis of ACF and PACF

  • Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)

  • Machine Learning Models: Logistic Regression, Support Vector Machines, Random Forests

  • Deep Learning Models: Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks

  • Implementation of GRUs and LSTMs for Time Series Forecasting

Practical Applications

The course explores real-world applications such as:

  • Forecasting sales figures using time series data

  • Predicting stock prices and returns

  • Classifying smartphone data to infer user behavior patterns

Exclusive VIP Content

The VIP version offers additional exclusive topics, including:

  • AWS Forecast, Amazon’s advanced low-code forecasting API

  • GARCH, focusing on financial volatility modeling

  • FB Prophet, Facebook’s renowned time series library

About the Author: Udemy

Udemy thumbnail

Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

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#1: Introduction and Outline
All Course Lessons (170)
#Lesson TitleDurationAccess
1
Introduction and Outline Demo
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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Course content

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

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Frequently asked questions

What is Time Series Analysis, Forecasting, and Machine Learning about?
Unveiling a new era in time series analysis , this course is designed to transcend traditional methods and immerse you in revolutionary advancements like deep learning and time series classification. From interpreting smartphone data for…
Who teaches Time Series Analysis, Forecasting, and Machine Learning?
Time Series Analysis, Forecasting, and Machine Learning is taught by Udemy. You can find more courses by this instructor on the corresponding source page.
How long is Time Series Analysis, Forecasting, and Machine Learning?
Time Series Analysis, Forecasting, and Machine Learning contains 170 lessons with a total runtime of 22 hours 47 minutes. All lessons are available to watch online at your own pace.
Is Time Series Analysis, Forecasting, and Machine Learning free to watch?
Time Series Analysis, Forecasting, and Machine Learning is part of CourseFlix's premium catalog. A CourseFlix subscription unlocks the full video player; the course description, table of contents, and preview information are available to everyone.
Where can I watch Time Series Analysis, Forecasting, and Machine Learning online?
Time Series Analysis, Forecasting, and Machine Learning is available to watch online on CourseFlix at https://courseflix.net/course/time-series-analysis-forecasting-and-machine-learning. The page hosts every lesson with the integrated video player; no download is required.