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Please note that this page does not hosts or makes available any of the listed filenames. You
cannot download any of those files from here.
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| [CourseClub.Me].url |
122B |
| [CourseClub.Me].url |
122B |
| [FreeCourseSite.com].url |
127B |
| [FreeCourseSite.com].url |
127B |
| [GigaCourse.Com].url |
49B |
| [GigaCourse.Com].url |
49B |
| 1.1 Code Link.html |
125B |
| 1.1 Data Links.html |
157B |
| 1.2 Data Links.html |
157B |
| 1.2 Github Links.html |
143B |
| 1.3 Github Link.html |
143B |
| 1. Anaconda Environment Setup.mp4 |
27.88MB |
| 1. Anaconda Environment Setup.srt |
20.26KB |
| 1. ARIMA Section Introduction.mp4 |
23.01MB |
| 1. ARIMA Section Introduction.srt |
7.15KB |
| 1. Artificial Neural Networks Section Introduction.mp4 |
19.43MB |
| 1. Artificial Neural Networks Section Introduction.srt |
4.38KB |
| 1. AWS Forecast Section Introduction.mp4 |
43.54MB |
| 1. AWS Forecast Section Introduction.srt |
10.63KB |
| 1. CNN Section Introduction.mp4 |
14.31MB |
| 1. CNN Section Introduction.srt |
4.04KB |
| 1. Exponential Smoothing Section Introduction.mp4 |
13.57MB |
| 1. Exponential Smoothing Section Introduction.srt |
3.87KB |
| 1. GARCH Section Introduction.mp4 |
18.20MB |
| 1. GARCH Section Introduction.srt |
5.16KB |
| 1. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 |
43.57MB |
| 1. Get Your Hands Dirty, Practical Coding Experience, Data Links.srt |
11.99KB |
| 1. How to Succeed in this Course (Long Version).mp4 |
12.61MB |
| 1. How to Succeed in this Course (Long Version).srt |
14.63KB |
| 1. Introduction and Outline.mp4 |
32.56MB |
| 1. Introduction and Outline.srt |
7.63KB |
| 1. Machine Learning Section Introduction.mp4 |
17.53MB |
| 1. Machine Learning Section Introduction.srt |
5.33KB |
| 1. Prophet Section Introduction.mp4 |
14.45MB |
| 1. Prophet Section Introduction.srt |
4.12KB |
| 1. RNN Section Introduction.mp4 |
20.52MB |
| 1. RNN Section Introduction.srt |
6.37KB |
| 1. Time Series Basics Section Introduction.mp4 |
18.85MB |
| 1. Time Series Basics Section Introduction.srt |
6.29KB |
| 1. Vector Autoregression Section Introduction.mp4 |
12.34MB |
| 1. Vector Autoregression Section Introduction.srt |
3.12KB |
| 1. What is the Appendix.mp4 |
16.40MB |
| 1. What is the Appendix.srt |
3.77KB |
| 1. Where to get the code, notebooks, and data.mp4 |
17.77MB |
| 1. Where to get the code, notebooks, and data.srt |
4.29KB |
| 10. (The Dangers of) Prophet for Stock Price Prediction.mp4 |
90.95MB |
| 10. (The Dangers of) Prophet for Stock Price Prediction.srt |
13.99KB |
| 10. ACF and PACF in Code (pt 1).mp4 |
41.31MB |
| 10. ACF and PACF in Code (pt 1).srt |
9.34KB |
| 10. CNN for Human Activity Recognition.mp4 |
46.39MB |
| 10. CNN for Human Activity Recognition.srt |
6.44KB |
| 10. Converting Between Models (Optional).mp4 |
37.16MB |
| 10. Converting Between Models (Optional).srt |
14.73KB |
| 10. Forecasting with Differencing.mp4 |
18.97MB |
| 10. Forecasting with Differencing.srt |
5.29KB |
| 10. GARCH Code (pt 3).mp4 |
43.96MB |
| 10. GARCH Code (pt 3).srt |
7.10KB |
| 10. Holt's Linear Trend Model (Code).mp4 |
19.05MB |
| 10. Holt's Linear Trend Model (Code).srt |
3.44KB |
| 10. Human Activity Recognition Dataset.mp4 |
30.74MB |
| 10. Human Activity Recognition Dataset.srt |
7.26KB |
| 10. LSTMs for Time Series Classification in Code.mp4 |
44.06MB |
| 10. LSTMs for Time Series Classification in Code.srt |
5.43KB |
| 10. Price Simulations in Code.mp4 |
18.28MB |
| 10. Price Simulations in Code.srt |
3.42KB |
| 11.1 Convert a Time Series Into an Image with Gramian Angular Fields and Markov Transition Fields.html |
123B |
| 11. ACF and PACF in Code (pt 2).mp4 |
33.88MB |
| 11. ACF and PACF in Code (pt 2).srt |
8.02KB |
| 11. CNN Section Summary.mp4 |
15.43MB |
| 11. CNN Section Summary.srt |
4.15KB |
| 11. GARCH Code (pt 4).mp4 |
41.27MB |
| 11. GARCH Code (pt 4).srt |
5.87KB |
| 11. Holt-Winters (Theory).mp4 |
47.55MB |
| 11. Holt-Winters (Theory).srt |
15.00KB |
| 11. Human Activity Recognition Code Preparation.mp4 |
31.27MB |
| 11. Human Activity Recognition Code Preparation.srt |
7.91KB |
| 11. Machine Learning for Time Series Forecasting in Code (pt 2).mp4 |
49.41MB |
| 11. Machine Learning for Time Series Forecasting in Code (pt 2).srt |
6.65KB |
| 11. Prophet Section Summary.mp4 |
13.47MB |
| 11. Prophet Section Summary.srt |
4.50KB |
| 11. Random Walks and the Random Walk Hypothesis.mp4 |
68.11MB |
| 11. Random Walks and the Random Walk Hypothesis.srt |
19.37KB |
| 11. The Unreasonable Ineffectiveness of Recurrent Neural Networks.mp4 |
15.45MB |
| 11. The Unreasonable Ineffectiveness of Recurrent Neural Networks.srt |
4.18KB |
| 11. Vector Autoregression Section Summary.mp4 |
18.68MB |
| 11. Vector Autoregression Section Summary.srt |
4.67KB |
| 12. Application Sales Data.mp4 |
42.19MB |
| 12. Application Sales Data.srt |
5.32KB |
| 12. Auto ARIMA and SARIMAX.mp4 |
39.45MB |
| 12. Auto ARIMA and SARIMAX.srt |
12.28KB |
| 12. GARCH Code (pt 5).mp4 |
31.90MB |
| 12. GARCH Code (pt 5).srt |
4.11KB |
| 12. Holt-Winters (Code).mp4 |
49.80MB |
| 12. Holt-Winters (Code).srt |
9.57KB |
| 12. Human Activity Recognition Data Exploration.mp4 |
49.95MB |
| 12. Human Activity Recognition Data Exploration.srt |
8.58KB |
| 12. RNN Section Summary.mp4 |
15.93MB |
| 12. RNN Section Summary.srt |
3.81KB |
| 12. The Naive Forecast and the Importance of Baselines.mp4 |
30.11MB |
| 12. The Naive Forecast and the Importance of Baselines.srt |
9.22KB |
| 13. A Deep Learning Approach to GARCH.mp4 |
46.09MB |
| 13. A Deep Learning Approach to GARCH.srt |
15.02KB |
| 13. Application Predicting Stock Prices and Returns.mp4 |
37.36MB |
| 13. Application Predicting Stock Prices and Returns.srt |
4.83KB |
| 13. Human Activity Recognition Multi-Input ANN.mp4 |
67.55MB |
| 13. Human Activity Recognition Multi-Input ANN.srt |
13.44KB |
| 13. Model Selection, AIC and BIC.mp4 |
45.91MB |
| 13. Model Selection, AIC and BIC.srt |
13.45KB |
| 13. Naive Forecast and Forecasting Metrics in Code.mp4 |
41.47MB |
| 13. Naive Forecast and Forecasting Metrics in Code.srt |
8.27KB |
| 13. Walk-Forward Validation.mp4 |
44.31MB |
| 13. Walk-Forward Validation.srt |
12.33KB |
| 14. Application Predicting Stock Movements.mp4 |
26.28MB |
| 14. Application Predicting Stock Movements.srt |
4.49KB |
| 14. Auto ARIMA in Code.mp4 |
103.19MB |
| 14. Auto ARIMA in Code.srt |
15.74KB |
| 14. GARCH Section Summary.mp4 |
30.82MB |
| 14. GARCH Section Summary.srt |
8.69KB |
| 14. Human Activity Recognition Feature-Based Model.mp4 |
36.06MB |
| 14. Human Activity Recognition Feature-Based Model.srt |
5.52KB |
| 14. Time Series Basics Section Summary.mp4 |
12.13MB |
| 14. Time Series Basics Section Summary.srt |
4.33KB |
| 14. Walk-Forward Validation in Code.mp4 |
60.26MB |
| 14. Walk-Forward Validation in Code.srt |
10.05KB |
| 15. Application Sales Data.mp4 |
29.44MB |
| 15. Application Sales Data.srt |
5.21KB |
| 15. Auto ARIMA in Code (Stocks).mp4 |
105.22MB |
| 15. Auto ARIMA in Code (Stocks).srt |
17.09KB |
| 15. Human Activity Recognition Combined Model.mp4 |
20.90MB |
| 15. Human Activity Recognition Combined Model.srt |
3.02KB |
| 15. Machine Learning Section Summary.mp4 |
10.36MB |
| 15. Machine Learning Section Summary.srt |
3.01KB |
| 15. Suggestion Box.mp4 |
27.16MB |
| 15. Suggestion Box.srt |
4.75KB |
| 16. ACF and PACF for Stock Returns.mp4 |
43.50MB |
| 16. ACF and PACF for Stock Returns.srt |
7.48KB |
| 16. Application Stock Predictions.mp4 |
40.51MB |
| 16. Application Stock Predictions.srt |
6.31KB |
| 16. How Does a Neural Network Learn.mp4 |
50.07MB |
| 16. How Does a Neural Network Learn.srt |
14.16KB |
| 17. Artificial Neural Networks Section Summary.mp4 |
10.95MB |
| 17. Artificial Neural Networks Section Summary.srt |
2.82KB |
| 17. Auto ARIMA in Code (Sales Data).mp4 |
65.42MB |
| 17. Auto ARIMA in Code (Sales Data).srt |
10.16KB |
| 17. SMA Application COVID-19 Counting.mp4 |
19.37MB |
| 17. SMA Application COVID-19 Counting.srt |
4.21KB |
| 18. How to Forecast with ARIMA.mp4 |
37.95MB |
| 18. How to Forecast with ARIMA.srt |
12.14KB |
| 18. SMA Application Algorithmic Trading.mp4 |
11.59MB |
| 18. SMA Application Algorithmic Trading.srt |
2.85KB |
| 19. Exponential Smoothing Section Summary.mp4 |
19.12MB |
| 19. Exponential Smoothing Section Summary.srt |
5.41KB |
| 19. Forecasting Out-Of-Sample.mp4 |
6.73MB |
| 19. Forecasting Out-Of-Sample.srt |
1.67KB |
| 2. ARCH Theory (pt 1).mp4 |
19.52MB |
| 2. ARCH Theory (pt 1).srt |
6.33KB |
| 2. Autoregressive Models - AR(p).mp4 |
52.54MB |
| 2. Autoregressive Models - AR(p).srt |
16.67KB |
| 2. BONUS.mp4 |
39.92MB |
| 2. BONUS.srt |
7.87KB |
| 2. Data Model.mp4 |
48.96MB |
| 2. Data Model.srt |
12.23KB |
| 2. Exponential Smoothing Intuition for Beginners.mp4 |
23.91MB |
| 2. Exponential Smoothing Intuition for Beginners.srt |
7.24KB |
| 2. How does Prophet work.mp4 |
40.74MB |
| 2. How does Prophet work.srt |
10.80KB |
| 2. How to Code by Yourself (part 1).mp4 |
24.59MB |
| 2. How to Code by Yourself (part 1).srt |
22.61KB |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
43.61MB |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt |
14.24KB |
| 2. How to use Github & Extra Coding Tips (Optional).mp4 |
63.89MB |
| 2. How to use Github & Extra Coding Tips (Optional).srt |
15.71KB |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
38.95MB |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt |
31.86KB |
| 2. Simple RNN Elman Unit (pt 1).mp4 |
38.74MB |
| 2. Simple RNN Elman Unit (pt 1).srt |
11.52KB |
| 2. Supervised Machine Learning Classification and Regression.mp4 |
68.96MB |
| 2. Supervised Machine Learning Classification and Regression.srt |
18.94KB |
| 2. The Neuron.mp4 |
43.86MB |
| 2. The Neuron.srt |
12.66KB |
| 2. VAR and VARMA Theory.mp4 |
59.22MB |
| 2. VAR and VARMA Theory.srt |
17.74KB |
| 2. Warmup (Optional).mp4 |
24.71MB |
| 2. Warmup (Optional).srt |
6.13KB |
| 2. What is a Time Series.mp4 |
32.24MB |
| 2. What is a Time Series.srt |
6.34KB |
| 2. What is Convolution.mp4 |
78.29MB |
| 2. What is Convolution.srt |
20.66KB |
| 20. (Optional) More About State-Space Models.mp4 |
40.17MB |
| 20. (Optional) More About State-Space Models.srt |
14.29KB |
| 20. ARIMA Section Summary.mp4 |
12.74MB |
| 20. ARIMA Section Summary.srt |
4.57KB |
| 3. ARCH Theory (pt 2).mp4 |
27.15MB |
| 3. ARCH Theory (pt 2).srt |
9.58KB |
| 3. Autoregressive Machine Learning Models.mp4 |
32.38MB |
| 3. Autoregressive Machine Learning Models.srt |
10.13KB |
| 3. Creating an IAM Role.mp4 |
23.80MB |
| 3. Creating an IAM Role.srt |
4.80KB |
| 3. Forward Propagation.mp4 |
44.79MB |
| 3. Forward Propagation.srt |
12.45KB |
| 3. How to Code by Yourself (part 2).mp4 |
49.18MB |
| 3. How to Code by Yourself (part 2).srt |
13.18KB |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 |
79.62MB |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt |
16.77KB |
| 3. Modeling vs. Predicting.mp4 |
13.48MB |
| 3. Modeling vs. Predicting.srt |
3.27KB |
| 3. Moving Average Models - MA(q).mp4 |
10.90MB |
| 3. Moving Average Models - MA(q).srt |
4.24KB |
| 3. Prophet Code Preparation.mp4 |
63.90MB |
| 3. Prophet Code Preparation.srt |
16.17KB |
| 3. Simple RNN Elman Unit (pt 2).mp4 |
40.01MB |
| 3. Simple RNN Elman Unit (pt 2).srt |
12.90KB |
| 3. SMA Theory.mp4 |
15.24MB |
| 3. SMA Theory.srt |
4.84KB |
| 3. VARMA Code (pt 1).mp4 |
49.32MB |
| 3. VARMA Code (pt 1).srt |
8.54KB |
| 3. What is Convolution (Pattern-Matching).mp4 |
24.07MB |
| 3. What is Convolution (Pattern-Matching).srt |
7.23KB |
| 4. ARCH Theory (pt 3).mp4 |
19.54MB |
| 4. ARCH Theory (pt 3).srt |
6.64KB |
| 4. ARIMA.mp4 |
41.39MB |
| 4. ARIMA.srt |
13.80KB |
| 4. Aside State Space Models vs. RNNs.mp4 |
18.62MB |
| 4. Aside State Space Models vs. RNNs.srt |
4.37KB |
| 4. Code pt 1 (Getting and Transforming the Data).mp4 |
63.34MB |
| 4. Code pt 1 (Getting and Transforming the Data).srt |
12.88KB |
| 4. Machine Learning Algorithms Linear Regression.mp4 |
21.80MB |
| 4. Machine Learning Algorithms Linear Regression.srt |
6.45KB |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 |
108.19MB |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt |
23.55KB |
| 4. Proof that using Jupyter Notebook is the same as not using it.mp4 |
69.51MB |
| 4. Proof that using Jupyter Notebook is the same as not using it.srt |
14.05KB |
| 4. Prophet in Code Data Preparation.mp4 |
54.73MB |
| 4. Prophet in Code Data Preparation.srt |
9.64KB |
| 4. SMA Code.mp4 |
54.10MB |
| 4. SMA Code.srt |
9.65KB |
| 4. The Geometrical Picture.mp4 |
53.97MB |
| 4. The Geometrical Picture.srt |
11.74KB |
| 4. VARMA Code (pt 2).mp4 |
52.25MB |
| 4. VARMA Code (pt 2).srt |
7.06KB |
| 4. What is Convolution (Weight Sharing).mp4 |
29.81MB |
| 4. What is Convolution (Weight Sharing).srt |
8.07KB |
| 4. Why Do We Care About Shapes.mp4 |
29.48MB |
| 4. Why Do We Care About Shapes.srt |
7.65KB |
| 5. Activation Functions.mp4 |
86.55MB |
| 5. Activation Functions.srt |
22.86KB |
| 5. ARIMA in Code.mp4 |
121.58MB |
| 5. ARIMA in Code.srt |
22.87KB |
| 5. Code pt 2 (Uploading the data to S3).mp4 |
91.06MB |
| 5. Code pt 2 (Uploading the data to S3).srt |
16.43KB |
| 5. Convolution on Color Images.mp4 |
75.65MB |
| 5. Convolution on Color Images.srt |
21.01KB |
| 5. EWMA Theory.mp4 |
35.83MB |
| 5. EWMA Theory.srt |
14.60KB |
| 5. GARCH Theory.mp4 |
27.48MB |
| 5. GARCH Theory.srt |
9.61KB |
| 5. Machine Learning Algorithms Logistic Regression.mp4 |
31.74MB |
| 5. Machine Learning Algorithms Logistic Regression.srt |
9.02KB |
| 5. Prophet in Code Fit, Forecast, Plot.mp4 |
55.21MB |
| 5. Prophet in Code Fit, Forecast, Plot.srt |
9.32KB |
| 5. RNN Code Preparation.mp4 |
34.14MB |
| 5. RNN Code Preparation.srt |
11.10KB |
| 5. Types of Tasks.mp4 |
23.55MB |
| 5. Types of Tasks.srt |
8.88KB |
| 5. VARMA Code (pt 3).mp4 |
45.43MB |
| 5. VARMA Code (pt 3).srt |
7.39KB |
| 6. Code pt 3 (Building your Model).mp4 |
54.47MB |
| 6. Code pt 3 (Building your Model).srt |
9.24KB |
| 6. Convolution for Time Series and ARIMA.mp4 |
23.61MB |
| 6. Convolution for Time Series and ARIMA.srt |
6.42KB |
| 6. EWMA Code.mp4 |
39.41MB |
| 6. EWMA Code.srt |
9.57KB |
| 6. GARCH Code Preparation (pt 1).mp4 |
37.92MB |
| 6. GARCH Code Preparation (pt 1).srt |
10.47KB |
| 6. Machine Learning Algorithms Support Vector Machines.mp4 |
43.52MB |
| 6. Machine Learning Algorithms Support Vector Machines.srt |
13.15KB |
| 6. Multiclass Classification.mp4 |
43.63MB |
| 6. Multiclass Classification.srt |
11.10KB |
| 6. Power, Log, and Box-Cox Transformations.mp4 |
32.63MB |
| 6. Power, Log, and Box-Cox Transformations.srt |
8.10KB |
| 6. Prophet in Code Holidays and Exogenous Regressors.mp4 |
67.92MB |
| 6. Prophet in Code Holidays and Exogenous Regressors.srt |
11.34KB |
| 6. RNNs Understanding by Implementing (Paying Attention to Shapes).mp4 |
55.52MB |
| 6. RNNs Understanding by Implementing (Paying Attention to Shapes).srt |
10.01KB |
| 6. Stationarity.mp4 |
55.15MB |
| 6. Stationarity.srt |
17.53KB |
| 6. VARMA Econometrics Code (pt 1).mp4 |
50.84MB |
| 6. VARMA Econometrics Code (pt 1).srt |
9.71KB |
| 7. ANN Code Preparation.mp4 |
57.51MB |
| 7. ANN Code Preparation.srt |
16.28KB |
| 7. CNN Architecture.mp4 |
96.82MB |
| 7. CNN Architecture.srt |
32.02KB |
| 7. Code pt 4 (Generating and Evaluating the Forecast).mp4 |
49.88MB |
| 7. Code pt 4 (Generating and Evaluating the Forecast).srt |
8.65KB |
| 7. GARCH Code Preparation (pt 2).mp4 |
40.01MB |
| 7. GARCH Code Preparation (pt 2).srt |
10.35KB |
| 7. GRU and LSTM (pt 1).mp4 |
80.02MB |
| 7. GRU and LSTM (pt 1).srt |
22.85KB |
| 7. Machine Learning Algorithms Random Forest.mp4 |
32.02MB |
| 7. Machine Learning Algorithms Random Forest.srt |
9.07KB |
| 7. Power, Log, and Box-Cox Transformations in Code.mp4 |
33.29MB |
| 7. Power, Log, and Box-Cox Transformations in Code.srt |
6.81KB |
| 7. Prophet in Code Cross-Validation.mp4 |
41.94MB |
| 7. Prophet in Code Cross-Validation.srt |
6.06KB |
| 7. SES Theory.mp4 |
35.57MB |
| 7. SES Theory.srt |
13.85KB |
| 7. Stationarity in Code.mp4 |
61.50MB |
| 7. Stationarity in Code.srt |
10.76KB |
| 7. VARMA Econometrics Code (pt 2).mp4 |
61.60MB |
| 7. VARMA Econometrics Code (pt 2).srt |
10.46KB |
| 8. ACF (Autocorrelation Function).mp4 |
37.00MB |
| 8. ACF (Autocorrelation Function).srt |
12.98KB |
| 8. AWS Forecast Exercise.mp4 |
13.76MB |
| 8. AWS Forecast Exercise.srt |
3.65KB |
| 8. CNN Code Preparation.mp4 |
27.49MB |
| 8. CNN Code Preparation.srt |
7.92KB |
| 8. Extrapolation and Stock Prices.mp4 |
64.73MB |
| 8. Extrapolation and Stock Prices.srt |
9.79KB |
| 8. Feedforward ANN for Time Series Forecasting Code.mp4 |
70.91MB |
| 8. Feedforward ANN for Time Series Forecasting Code.srt |
10.74KB |
| 8. Forecasting Metrics.mp4 |
43.69MB |
| 8. Forecasting Metrics.srt |
15.23KB |
| 8. GARCH Code (pt 1).mp4 |
33.26MB |
| 8. GARCH Code (pt 1).srt |
6.11KB |
| 8. Granger Causality.mp4 |
22.42MB |
| 8. Granger Causality.srt |
5.29KB |
| 8. GRU and LSTM (pt 2).mp4 |
50.24MB |
| 8. GRU and LSTM (pt 2).srt |
14.81KB |
| 8. Prophet in Code Changepoint Detection.mp4 |
37.96MB |
| 8. Prophet in Code Changepoint Detection.srt |
4.23KB |
| 8. SES Code.mp4 |
69.54MB |
| 8. SES Code.srt |
14.53KB |
| 9. AWS Forecast Section Summary.mp4 |
25.46MB |
| 9. AWS Forecast Section Summary.srt |
6.81KB |
| 9. CNN for Time Series Forecasting in Code.mp4 |
48.78MB |
| 9. CNN for Time Series Forecasting in Code.srt |
6.87KB |
| 9. Feedforward ANN for Stock Return and Price Predictions Code.mp4 |
67.71MB |
| 9. Feedforward ANN for Stock Return and Price Predictions Code.srt |
9.03KB |
| 9. Financial Time Series Primer.mp4 |
44.86MB |
| 9. Financial Time Series Primer.srt |
15.01KB |
| 9. GARCH Code (pt 2).mp4 |
51.93MB |
| 9. GARCH Code (pt 2).srt |
8.68KB |
| 9. Granger Causality Code.mp4 |
32.00MB |
| 9. Granger Causality Code.srt |
3.47KB |
| 9. Holt's Linear Trend Model (Theory).mp4 |
33.20MB |
| 9. Holt's Linear Trend Model (Theory).srt |
10.08KB |
| 9. LSTMs for Time Series Forecasting in Code.mp4 |
197.71MB |
| 9. LSTMs for Time Series Forecasting in Code.srt |
34.37KB |
| 9. Machine Learning for Time Series Forecasting in Code (pt 1).mp4 |
86.17MB |
| 9. Machine Learning for Time Series Forecasting in Code (pt 1).srt |
14.96KB |
| 9. PACF (Partial Autocorrelation Funtion).mp4 |
25.11MB |
| 9. PACF (Partial Autocorrelation Funtion).srt |
7.97KB |
| 9. Prophet Multiplicative Seasonality, Outliers, Non-Daily Data.mp4 |
67.80MB |
| 9. Prophet Multiplicative Seasonality, Outliers, Non-Daily Data.srt |
9.62KB |