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[] Udemy - Time Series Analysis, Forecasting, and Machine Learning

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文件列表

  1. 10. Deep Learning Recurrent Neural Networks (RNN)/9. LSTMs for Time Series Forecasting in Code.mp4 198MB
  2. 5. ARIMA/5. ARIMA in Code.mp4 122MB
  3. 16. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 108MB
  4. 5. ARIMA/15. Auto ARIMA in Code (Stocks).mp4 105MB
  5. 5. ARIMA/14. Auto ARIMA in Code.mp4 103MB
  6. 9. Deep Learning Convolutional Neural Networks (CNN)/7. CNN Architecture.mp4 97MB
  7. 12. VIP AWS Forecast/5. Code pt 2 (Uploading the data to S3).mp4 91MB
  8. 13. VIP Facebook Prophet/10. (The Dangers of) Prophet for Stock Price Prediction.mp4 91MB
  9. 8. Deep Learning Artificial Neural Networks (ANN)/5. Activation Functions.mp4 87MB
  10. 7. Machine Learning Methods/9. Machine Learning for Time Series Forecasting in Code (pt 1).mp4 86MB
  11. 10. Deep Learning Recurrent Neural Networks (RNN)/7. GRU and LSTM (pt 1).mp4 80MB
  12. 16. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 80MB
  13. 9. Deep Learning Convolutional Neural Networks (CNN)/2. What is Convolution.mp4 78MB
  14. 9. Deep Learning Convolutional Neural Networks (CNN)/5. Convolution on Color Images.mp4 76MB
  15. 8. Deep Learning Artificial Neural Networks (ANN)/8. Feedforward ANN for Time Series Forecasting Code.mp4 71MB
  16. 4. Exponential Smoothing and ETS Methods/8. SES Code.mp4 70MB
  17. 15. Extra Help With Python Coding for Beginners FAQ/4. Proof that using Jupyter Notebook is the same as not using it.mp4 70MB
  18. 7. Machine Learning Methods/2. Supervised Machine Learning Classification and Regression.mp4 69MB
  19. 3. Time Series Basics/11. Random Walks and the Random Walk Hypothesis.mp4 68MB
  20. 13. VIP Facebook Prophet/6. Prophet in Code Holidays and Exogenous Regressors.mp4 68MB
  21. 13. VIP Facebook Prophet/9. Prophet Multiplicative Seasonality, Outliers, Non-Daily Data.mp4 68MB
  22. 8. Deep Learning Artificial Neural Networks (ANN)/9. Feedforward ANN for Stock Return and Price Predictions Code.mp4 68MB
  23. 8. Deep Learning Artificial Neural Networks (ANN)/13. Human Activity Recognition Multi-Input ANN.mp4 68MB
  24. 5. ARIMA/17. Auto ARIMA in Code (Sales Data).mp4 65MB
  25. 7. Machine Learning Methods/8. Extrapolation and Stock Prices.mp4 65MB
  26. 13. VIP Facebook Prophet/3. Prophet Code Preparation.mp4 64MB
  27. 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).mp4 64MB
  28. 12. VIP AWS Forecast/4. Code pt 1 (Getting and Transforming the Data).mp4 63MB
  29. 6. Vector Autoregression (VAR, VMA, VARMA)/7. VARMA Econometrics Code (pt 2).mp4 62MB
  30. 5. ARIMA/7. Stationarity in Code.mp4 62MB
  31. 4. Exponential Smoothing and ETS Methods/14. Walk-Forward Validation in Code.mp4 60MB
  32. 6. Vector Autoregression (VAR, VMA, VARMA)/2. VAR and VARMA Theory.mp4 59MB
  33. 8. Deep Learning Artificial Neural Networks (ANN)/7. ANN Code Preparation.mp4 58MB
  34. 10. Deep Learning Recurrent Neural Networks (RNN)/6. RNNs Understanding by Implementing (Paying Attention to Shapes).mp4 56MB
  35. 13. VIP Facebook Prophet/5. Prophet in Code Fit, Forecast, Plot.mp4 55MB
  36. 5. ARIMA/6. Stationarity.mp4 55MB
  37. 13. VIP Facebook Prophet/4. Prophet in Code Data Preparation.mp4 55MB
  38. 12. VIP AWS Forecast/6. Code pt 3 (Building your Model).mp4 54MB
  39. 4. Exponential Smoothing and ETS Methods/4. SMA Code.mp4 54MB
  40. 8. Deep Learning Artificial Neural Networks (ANN)/4. The Geometrical Picture.mp4 54MB
  41. 5. ARIMA/2. Autoregressive Models - AR(p).mp4 53MB
  42. 6. Vector Autoregression (VAR, VMA, VARMA)/4. VARMA Code (pt 2).mp4 52MB
  43. 11. VIP GARCH/9. GARCH Code (pt 2).mp4 52MB
  44. 6. Vector Autoregression (VAR, VMA, VARMA)/6. VARMA Econometrics Code (pt 1).mp4 51MB
  45. 10. Deep Learning Recurrent Neural Networks (RNN)/8. GRU and LSTM (pt 2).mp4 50MB
  46. 8. Deep Learning Artificial Neural Networks (ANN)/16. How Does a Neural Network Learn.mp4 50MB
  47. 8. Deep Learning Artificial Neural Networks (ANN)/12. Human Activity Recognition Data Exploration.mp4 50MB
  48. 12. VIP AWS Forecast/7. Code pt 4 (Generating and Evaluating the Forecast).mp4 50MB
  49. 4. Exponential Smoothing and ETS Methods/12. Holt-Winters (Code).mp4 50MB
  50. 7. Machine Learning Methods/11. Machine Learning for Time Series Forecasting in Code (pt 2).mp4 49MB
  51. 6. Vector Autoregression (VAR, VMA, VARMA)/3. VARMA Code (pt 1).mp4 49MB
  52. 15. Extra Help With Python Coding for Beginners FAQ/3. How to Code by Yourself (part 2).mp4 49MB
  53. 12. VIP AWS Forecast/2. Data Model.mp4 49MB
  54. 9. Deep Learning Convolutional Neural Networks (CNN)/9. CNN for Time Series Forecasting in Code.mp4 49MB
  55. 4. Exponential Smoothing and ETS Methods/11. Holt-Winters (Theory).mp4 48MB
  56. 9. Deep Learning Convolutional Neural Networks (CNN)/10. CNN for Human Activity Recognition.mp4 46MB
  57. 11. VIP GARCH/13. A Deep Learning Approach to GARCH.mp4 46MB
  58. 5. ARIMA/13. Model Selection, AIC and BIC.mp4 46MB
  59. 6. Vector Autoregression (VAR, VMA, VARMA)/5. VARMA Code (pt 3).mp4 45MB
  60. 3. Time Series Basics/9. Financial Time Series Primer.mp4 45MB
  61. 8. Deep Learning Artificial Neural Networks (ANN)/3. Forward Propagation.mp4 45MB
  62. 4. Exponential Smoothing and ETS Methods/13. Walk-Forward Validation.mp4 44MB
  63. 10. Deep Learning Recurrent Neural Networks (RNN)/10. LSTMs for Time Series Classification in Code.mp4 44MB
  64. 11. VIP GARCH/10. GARCH Code (pt 3).mp4 44MB
  65. 8. Deep Learning Artificial Neural Networks (ANN)/2. The Neuron.mp4 44MB
  66. 3. Time Series Basics/8. Forecasting Metrics.mp4 44MB
  67. 8. Deep Learning Artificial Neural Networks (ANN)/6. Multiclass Classification.mp4 44MB
  68. 14. Setting Up Your Environment FAQ/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  69. 2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 44MB
  70. 12. VIP AWS Forecast/1. AWS Forecast Section Introduction.mp4 44MB
  71. 7. Machine Learning Methods/6. Machine Learning Algorithms Support Vector Machines.mp4 44MB
  72. 5. ARIMA/16. ACF and PACF for Stock Returns.mp4 43MB
  73. 7. Machine Learning Methods/12. Application Sales Data.mp4 42MB
  74. 13. VIP Facebook Prophet/7. Prophet in Code Cross-Validation.mp4 42MB
  75. 3. Time Series Basics/13. Naive Forecast and Forecasting Metrics in Code.mp4 41MB
  76. 5. ARIMA/4. ARIMA.mp4 41MB
  77. 5. ARIMA/10. ACF and PACF in Code (pt 1).mp4 41MB
  78. 11. VIP GARCH/11. GARCH Code (pt 4).mp4 41MB
  79. 13. VIP Facebook Prophet/2. How does Prophet work.mp4 41MB
  80. 4. Exponential Smoothing and ETS Methods/16. Application Stock Predictions.mp4 41MB
  81. 4. Exponential Smoothing and ETS Methods/20. (Optional) More About State-Space Models.mp4 40MB
  82. 10. Deep Learning Recurrent Neural Networks (RNN)/3. Simple RNN Elman Unit (pt 2).mp4 40MB
  83. 11. VIP GARCH/7. GARCH Code Preparation (pt 2).mp4 40MB
  84. 17. Appendix FAQ Finale/2. BONUS.mp4 40MB
  85. 5. ARIMA/12. Auto ARIMA and SARIMAX.mp4 39MB
  86. 4. Exponential Smoothing and ETS Methods/6. EWMA Code.mp4 39MB
  87. 16. Effective Learning Strategies for Machine Learning FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  88. 10. Deep Learning Recurrent Neural Networks (RNN)/2. Simple RNN Elman Unit (pt 1).mp4 39MB
  89. 13. VIP Facebook Prophet/8. Prophet in Code Changepoint Detection.mp4 38MB
  90. 5. ARIMA/18. How to Forecast with ARIMA.mp4 38MB
  91. 11. VIP GARCH/6. GARCH Code Preparation (pt 1).mp4 38MB
  92. 7. Machine Learning Methods/13. Application Predicting Stock Prices and Returns.mp4 37MB
  93. 6. Vector Autoregression (VAR, VMA, VARMA)/10. Converting Between Models (Optional).mp4 37MB
  94. 5. ARIMA/8. ACF (Autocorrelation Function).mp4 37MB
  95. 8. Deep Learning Artificial Neural Networks (ANN)/14. Human Activity Recognition Feature-Based Model.mp4 36MB
  96. 4. Exponential Smoothing and ETS Methods/5. EWMA Theory.mp4 36MB
  97. 4. Exponential Smoothing and ETS Methods/7. SES Theory.mp4 36MB
  98. 10. Deep Learning Recurrent Neural Networks (RNN)/5. RNN Code Preparation.mp4 34MB
  99. 5. ARIMA/11. ACF and PACF in Code (pt 2).mp4 34MB
  100. 3. Time Series Basics/7. Power, Log, and Box-Cox Transformations in Code.mp4 33MB
  101. 11. VIP GARCH/8. GARCH Code (pt 1).mp4 33MB
  102. 4. Exponential Smoothing and ETS Methods/9. Holt's Linear Trend Model (Theory).mp4 33MB
  103. 3. Time Series Basics/6. Power, Log, and Box-Cox Transformations.mp4 33MB
  104. 1. Welcome/1. Introduction and Outline.mp4 33MB
  105. 7. Machine Learning Methods/3. Autoregressive Machine Learning Models.mp4 32MB
  106. 3. Time Series Basics/2. What is a Time Series.mp4 32MB
  107. 7. Machine Learning Methods/7. Machine Learning Algorithms Random Forest.mp4 32MB
  108. 6. Vector Autoregression (VAR, VMA, VARMA)/9. Granger Causality Code.mp4 32MB
  109. 11. VIP GARCH/12. GARCH Code (pt 5).mp4 32MB
  110. 7. Machine Learning Methods/5. Machine Learning Algorithms Logistic Regression.mp4 32MB
  111. 8. Deep Learning Artificial Neural Networks (ANN)/11. Human Activity Recognition Code Preparation.mp4 31MB
  112. 11. VIP GARCH/14. GARCH Section Summary.mp4 31MB
  113. 8. Deep Learning Artificial Neural Networks (ANN)/10. Human Activity Recognition Dataset.mp4 31MB
  114. 3. Time Series Basics/12. The Naive Forecast and the Importance of Baselines.mp4 30MB
  115. 9. Deep Learning Convolutional Neural Networks (CNN)/4. What is Convolution (Weight Sharing).mp4 30MB
  116. 3. Time Series Basics/4. Why Do We Care About Shapes.mp4 29MB
  117. 4. Exponential Smoothing and ETS Methods/15. Application Sales Data.mp4 29MB
  118. 14. Setting Up Your Environment FAQ/1. Anaconda Environment Setup.mp4 28MB
  119. 9. Deep Learning Convolutional Neural Networks (CNN)/8. CNN Code Preparation.mp4 27MB
  120. 11. VIP GARCH/5. GARCH Theory.mp4 27MB
  121. 3. Time Series Basics/15. Suggestion Box.mp4 27MB
  122. 11. VIP GARCH/3. ARCH Theory (pt 2).mp4 27MB
  123. 7. Machine Learning Methods/14. Application Predicting Stock Movements.mp4 26MB
  124. 12. VIP AWS Forecast/9. AWS Forecast Section Summary.mp4 25MB
  125. 5. ARIMA/9. PACF (Partial Autocorrelation Funtion).mp4 25MB
  126. 1. Welcome/2. Warmup (Optional).mp4 25MB
  127. 15. Extra Help With Python Coding for Beginners FAQ/2. How to Code by Yourself (part 1).mp4 25MB
  128. 9. Deep Learning Convolutional Neural Networks (CNN)/3. What is Convolution (Pattern-Matching).mp4 24MB
  129. 4. Exponential Smoothing and ETS Methods/2. Exponential Smoothing Intuition for Beginners.mp4 24MB
  130. 12. VIP AWS Forecast/3. Creating an IAM Role.mp4 24MB
  131. 9. Deep Learning Convolutional Neural Networks (CNN)/6. Convolution for Time Series and ARIMA.mp4 24MB
  132. 3. Time Series Basics/5. Types of Tasks.mp4 24MB
  133. 5. ARIMA/1. ARIMA Section Introduction.mp4 23MB
  134. 6. Vector Autoregression (VAR, VMA, VARMA)/8. Granger Causality.mp4 22MB
  135. 7. Machine Learning Methods/4. Machine Learning Algorithms Linear Regression.mp4 22MB
  136. 8. Deep Learning Artificial Neural Networks (ANN)/15. Human Activity Recognition Combined Model.mp4 21MB
  137. 10. Deep Learning Recurrent Neural Networks (RNN)/1. RNN Section Introduction.mp4 21MB
  138. 11. VIP GARCH/4. ARCH Theory (pt 3).mp4 20MB
  139. 11. VIP GARCH/2. ARCH Theory (pt 1).mp4 20MB
  140. 8. Deep Learning Artificial Neural Networks (ANN)/1. Artificial Neural Networks Section Introduction.mp4 19MB
  141. 4. Exponential Smoothing and ETS Methods/17. SMA Application COVID-19 Counting.mp4 19MB
  142. 4. Exponential Smoothing and ETS Methods/19. Exponential Smoothing Section Summary.mp4 19MB
  143. 4. Exponential Smoothing and ETS Methods/10. Holt's Linear Trend Model (Code).mp4 19MB
  144. 7. Machine Learning Methods/10. Forecasting with Differencing.mp4 19MB
  145. 3. Time Series Basics/1. Time Series Basics Section Introduction.mp4 19MB
  146. 6. Vector Autoregression (VAR, VMA, VARMA)/11. Vector Autoregression Section Summary.mp4 19MB
  147. 10. Deep Learning Recurrent Neural Networks (RNN)/4. Aside State Space Models vs. RNNs.mp4 19MB
  148. 3. Time Series Basics/10. Price Simulations in Code.mp4 18MB
  149. 11. VIP GARCH/1. GARCH Section Introduction.mp4 18MB
  150. 15. Extra Help With Python Coding for Beginners FAQ/1. Where to get the code, notebooks, and data.mp4 18MB
  151. 7. Machine Learning Methods/1. Machine Learning Section Introduction.mp4 18MB
  152. 17. Appendix FAQ Finale/1. What is the Appendix.mp4 16MB
  153. 10. Deep Learning Recurrent Neural Networks (RNN)/12. RNN Section Summary.mp4 16MB
  154. 10. Deep Learning Recurrent Neural Networks (RNN)/11. The Unreasonable Ineffectiveness of Recurrent Neural Networks.mp4 15MB
  155. 9. Deep Learning Convolutional Neural Networks (CNN)/11. CNN Section Summary.mp4 15MB
  156. 4. Exponential Smoothing and ETS Methods/3. SMA Theory.mp4 15MB
  157. 13. VIP Facebook Prophet/1. Prophet Section Introduction.mp4 14MB
  158. 9. Deep Learning Convolutional Neural Networks (CNN)/1. CNN Section Introduction.mp4 14MB
  159. 12. VIP AWS Forecast/8. AWS Forecast Exercise.mp4 14MB
  160. 4. Exponential Smoothing and ETS Methods/1. Exponential Smoothing Section Introduction.mp4 14MB
  161. 3. Time Series Basics/3. Modeling vs. Predicting.mp4 13MB
  162. 13. VIP Facebook Prophet/11. Prophet Section Summary.mp4 13MB
  163. 5. ARIMA/20. ARIMA Section Summary.mp4 13MB
  164. 16. Effective Learning Strategies for Machine Learning FAQ/1. How to Succeed in this Course (Long Version).mp4 13MB
  165. 6. Vector Autoregression (VAR, VMA, VARMA)/1. Vector Autoregression Section Introduction.mp4 12MB
  166. 3. Time Series Basics/14. Time Series Basics Section Summary.mp4 12MB
  167. 4. Exponential Smoothing and ETS Methods/18. SMA Application Algorithmic Trading.mp4 12MB
  168. 8. Deep Learning Artificial Neural Networks (ANN)/17. Artificial Neural Networks Section Summary.mp4 11MB
  169. 5. ARIMA/3. Moving Average Models - MA(q).mp4 11MB
  170. 7. Machine Learning Methods/15. Machine Learning Section Summary.mp4 10MB
  171. 5. ARIMA/19. Forecasting Out-Of-Sample.mp4 7MB
  172. 10. Deep Learning Recurrent Neural Networks (RNN)/9. LSTMs for Time Series Forecasting in Code.srt 34KB
  173. 9. Deep Learning Convolutional Neural Networks (CNN)/7. CNN Architecture.srt 32KB
  174. 16. Effective Learning Strategies for Machine Learning FAQ/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32KB
  175. 16. Effective Learning Strategies for Machine Learning FAQ/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 24KB
  176. 5. ARIMA/5. ARIMA in Code.srt 23KB
  177. 8. Deep Learning Artificial Neural Networks (ANN)/5. Activation Functions.srt 23KB
  178. 10. Deep Learning Recurrent Neural Networks (RNN)/7. GRU and LSTM (pt 1).srt 23KB
  179. 15. Extra Help With Python Coding for Beginners FAQ/2. How to Code by Yourself (part 1).srt 23KB
  180. 9. Deep Learning Convolutional Neural Networks (CNN)/5. Convolution on Color Images.srt 21KB
  181. 9. Deep Learning Convolutional Neural Networks (CNN)/2. What is Convolution.srt 21KB
  182. 14. Setting Up Your Environment FAQ/1. Anaconda Environment Setup.srt 20KB
  183. 3. Time Series Basics/11. Random Walks and the Random Walk Hypothesis.srt 19KB
  184. 7. Machine Learning Methods/2. Supervised Machine Learning Classification and Regression.srt 19KB
  185. 6. Vector Autoregression (VAR, VMA, VARMA)/2. VAR and VARMA Theory.srt 18KB
  186. 5. ARIMA/6. Stationarity.srt 18KB
  187. 5. ARIMA/15. Auto ARIMA in Code (Stocks).srt 17KB
  188. 16. Effective Learning Strategies for Machine Learning FAQ/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 17KB
  189. 5. ARIMA/2. Autoregressive Models - AR(p).srt 17KB
  190. 12. VIP AWS Forecast/5. Code pt 2 (Uploading the data to S3).srt 16KB
  191. 8. Deep Learning Artificial Neural Networks (ANN)/7. ANN Code Preparation.srt 16KB
  192. 13. VIP Facebook Prophet/3. Prophet Code Preparation.srt 16KB
  193. 5. ARIMA/14. Auto ARIMA in Code.srt 16KB
  194. 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).srt 16KB
  195. 3. Time Series Basics/8. Forecasting Metrics.srt 15KB
  196. 11. VIP GARCH/13. A Deep Learning Approach to GARCH.srt 15KB
  197. 3. Time Series Basics/9. Financial Time Series Primer.srt 15KB
  198. 4. Exponential Smoothing and ETS Methods/11. Holt-Winters (Theory).srt 15KB
  199. 7. Machine Learning Methods/9. Machine Learning for Time Series Forecasting in Code (pt 1).srt 15KB
  200. 10. Deep Learning Recurrent Neural Networks (RNN)/8. GRU and LSTM (pt 2).srt 15KB
  201. 6. Vector Autoregression (VAR, VMA, VARMA)/10. Converting Between Models (Optional).srt 15KB
  202. 16. Effective Learning Strategies for Machine Learning FAQ/1. How to Succeed in this Course (Long Version).srt 15KB
  203. 4. Exponential Smoothing and ETS Methods/5. EWMA Theory.srt 15KB
  204. 4. Exponential Smoothing and ETS Methods/8. SES Code.srt 15KB
  205. 4. Exponential Smoothing and ETS Methods/20. (Optional) More About State-Space Models.srt 14KB
  206. 14. Setting Up Your Environment FAQ/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14KB
  207. 8. Deep Learning Artificial Neural Networks (ANN)/16. How Does a Neural Network Learn.srt 14KB
  208. 15. Extra Help With Python Coding for Beginners FAQ/4. Proof that using Jupyter Notebook is the same as not using it.srt 14KB
  209. 13. VIP Facebook Prophet/10. (The Dangers of) Prophet for Stock Price Prediction.srt 14KB
  210. 4. Exponential Smoothing and ETS Methods/7. SES Theory.srt 14KB
  211. 5. ARIMA/4. ARIMA.srt 14KB
  212. 5. ARIMA/13. Model Selection, AIC and BIC.srt 13KB
  213. 8. Deep Learning Artificial Neural Networks (ANN)/13. Human Activity Recognition Multi-Input ANN.srt 13KB
  214. 15. Extra Help With Python Coding for Beginners FAQ/3. How to Code by Yourself (part 2).srt 13KB
  215. 7. Machine Learning Methods/6. Machine Learning Algorithms Support Vector Machines.srt 13KB
  216. 5. ARIMA/8. ACF (Autocorrelation Function).srt 13KB
  217. 10. Deep Learning Recurrent Neural Networks (RNN)/3. Simple RNN Elman Unit (pt 2).srt 13KB
  218. 12. VIP AWS Forecast/4. Code pt 1 (Getting and Transforming the Data).srt 13KB
  219. 8. Deep Learning Artificial Neural Networks (ANN)/2. The Neuron.srt 13KB
  220. 8. Deep Learning Artificial Neural Networks (ANN)/3. Forward Propagation.srt 12KB
  221. 4. Exponential Smoothing and ETS Methods/13. Walk-Forward Validation.srt 12KB
  222. 5. ARIMA/12. Auto ARIMA and SARIMAX.srt 12KB
  223. 12. VIP AWS Forecast/2. Data Model.srt 12KB
  224. 5. ARIMA/18. How to Forecast with ARIMA.srt 12KB
  225. 2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.srt 12KB
  226. 8. Deep Learning Artificial Neural Networks (ANN)/4. The Geometrical Picture.srt 12KB
  227. 10. Deep Learning Recurrent Neural Networks (RNN)/2. Simple RNN Elman Unit (pt 1).srt 12KB
  228. 13. VIP Facebook Prophet/6. Prophet in Code Holidays and Exogenous Regressors.srt 11KB
  229. 10. Deep Learning Recurrent Neural Networks (RNN)/5. RNN Code Preparation.srt 11KB
  230. 8. Deep Learning Artificial Neural Networks (ANN)/6. Multiclass Classification.srt 11KB
  231. 13. VIP Facebook Prophet/2. How does Prophet work.srt 11KB
  232. 5. ARIMA/7. Stationarity in Code.srt 11KB
  233. 8. Deep Learning Artificial Neural Networks (ANN)/8. Feedforward ANN for Time Series Forecasting Code.srt 11KB
  234. 12. VIP AWS Forecast/1. AWS Forecast Section Introduction.srt 11KB
  235. 11. VIP GARCH/6. GARCH Code Preparation (pt 1).srt 10KB
  236. 6. Vector Autoregression (VAR, VMA, VARMA)/7. VARMA Econometrics Code (pt 2).srt 10KB
  237. 11. VIP GARCH/7. GARCH Code Preparation (pt 2).srt 10KB
  238. 5. ARIMA/17. Auto ARIMA in Code (Sales Data).srt 10KB
  239. 7. Machine Learning Methods/3. Autoregressive Machine Learning Models.srt 10KB
  240. 4. Exponential Smoothing and ETS Methods/9. Holt's Linear Trend Model (Theory).srt 10KB
  241. 4. Exponential Smoothing and ETS Methods/14. Walk-Forward Validation in Code.srt 10KB
  242. 10. Deep Learning Recurrent Neural Networks (RNN)/6. RNNs Understanding by Implementing (Paying Attention to Shapes).srt 10KB
  243. 7. Machine Learning Methods/8. Extrapolation and Stock Prices.srt 10KB
  244. 6. Vector Autoregression (VAR, VMA, VARMA)/6. VARMA Econometrics Code (pt 1).srt 10KB
  245. 4. Exponential Smoothing and ETS Methods/4. SMA Code.srt 10KB
  246. 13. VIP Facebook Prophet/4. Prophet in Code Data Preparation.srt 10KB
  247. 13. VIP Facebook Prophet/9. Prophet Multiplicative Seasonality, Outliers, Non-Daily Data.srt 10KB
  248. 11. VIP GARCH/5. GARCH Theory.srt 10KB
  249. 11. VIP GARCH/3. ARCH Theory (pt 2).srt 10KB
  250. 4. Exponential Smoothing and ETS Methods/6. EWMA Code.srt 10KB
  251. 4. Exponential Smoothing and ETS Methods/12. Holt-Winters (Code).srt 10KB
  252. 5. ARIMA/10. ACF and PACF in Code (pt 1).srt 9KB
  253. 13. VIP Facebook Prophet/5. Prophet in Code Fit, Forecast, Plot.srt 9KB
  254. 12. VIP AWS Forecast/6. Code pt 3 (Building your Model).srt 9KB
  255. 3. Time Series Basics/12. The Naive Forecast and the Importance of Baselines.srt 9KB
  256. 7. Machine Learning Methods/7. Machine Learning Algorithms Random Forest.srt 9KB
  257. 8. Deep Learning Artificial Neural Networks (ANN)/9. Feedforward ANN for Stock Return and Price Predictions Code.srt 9KB
  258. 7. Machine Learning Methods/5. Machine Learning Algorithms Logistic Regression.srt 9KB
  259. 3. Time Series Basics/5. Types of Tasks.srt 9KB
  260. 11. VIP GARCH/14. GARCH Section Summary.srt 9KB
  261. 11. VIP GARCH/9. GARCH Code (pt 2).srt 9KB
  262. 12. VIP AWS Forecast/7. Code pt 4 (Generating and Evaluating the Forecast).srt 9KB
  263. 8. Deep Learning Artificial Neural Networks (ANN)/12. Human Activity Recognition Data Exploration.srt 9KB
  264. 6. Vector Autoregression (VAR, VMA, VARMA)/3. VARMA Code (pt 1).srt 9KB
  265. 3. Time Series Basics/13. Naive Forecast and Forecasting Metrics in Code.srt 8KB
  266. 3. Time Series Basics/6. Power, Log, and Box-Cox Transformations.srt 8KB
  267. 9. Deep Learning Convolutional Neural Networks (CNN)/4. What is Convolution (Weight Sharing).srt 8KB
  268. 5. ARIMA/11. ACF and PACF in Code (pt 2).srt 8KB
  269. 5. ARIMA/9. PACF (Partial Autocorrelation Funtion).srt 8KB
  270. 9. Deep Learning Convolutional Neural Networks (CNN)/8. CNN Code Preparation.srt 8KB
  271. 8. Deep Learning Artificial Neural Networks (ANN)/11. Human Activity Recognition Code Preparation.srt 8KB
  272. 17. Appendix FAQ Finale/2. BONUS.srt 8KB
  273. 3. Time Series Basics/4. Why Do We Care About Shapes.srt 8KB
  274. 1. Welcome/1. Introduction and Outline.srt 8KB
  275. 5. ARIMA/16. ACF and PACF for Stock Returns.srt 7KB
  276. 6. Vector Autoregression (VAR, VMA, VARMA)/5. VARMA Code (pt 3).srt 7KB
  277. 8. Deep Learning Artificial Neural Networks (ANN)/10. Human Activity Recognition Dataset.srt 7KB
  278. 4. Exponential Smoothing and ETS Methods/2. Exponential Smoothing Intuition for Beginners.srt 7KB
  279. 9. Deep Learning Convolutional Neural Networks (CNN)/3. What is Convolution (Pattern-Matching).srt 7KB
  280. 5. ARIMA/1. ARIMA Section Introduction.srt 7KB
  281. 11. VIP GARCH/10. GARCH Code (pt 3).srt 7KB
  282. 6. Vector Autoregression (VAR, VMA, VARMA)/4. VARMA Code (pt 2).srt 7KB
  283. 9. Deep Learning Convolutional Neural Networks (CNN)/9. CNN for Time Series Forecasting in Code.srt 7KB
  284. 3. Time Series Basics/7. Power, Log, and Box-Cox Transformations in Code.srt 7KB
  285. 12. VIP AWS Forecast/9. AWS Forecast Section Summary.srt 7KB
  286. 7. Machine Learning Methods/11. Machine Learning for Time Series Forecasting in Code (pt 2).srt 7KB
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