Udemy Practical Python for Time Series Analysis and Modelling

[AD-TEAM]

1.29 GB | 00:07:20 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
1  Introduction  (13.98 MB)
10  Exam Energy prices by markets  (44.82 MB)
2  Load and preprocess temporal columns  (45.44 MB)
3  Pivot tables  (62.72 MB)
4  Automatic temporal resampling  (32.37 MB)
5  Resampling to group noise  (28.58 MB)
6  Correlation and interactive visualization with Plotly  (37.28 MB)
7  Melting pivot tables  (22.11 MB)
8  Correlation matrix  (83.82 MB)
9  Rankings with pivot tables  (37.14 MB)
1  Time series decomposition  (56.24 MB)
2  Component visualization  (36.16 MB)
3  Additive vs multiplicative model  (40.08 MB)
4  Exam Daily vs monthly solar generation  (14.91 MB)
1  Differencing a time series  (72.98 MB)
2  Exam Photovoltaic solar generation  (11.9 MB)
1  Baseline Models  (62.32 MB)
2  Statistical Models  (58.46 MB)
3  ARIMA Models  (29.18 MB)
4  ACF & PACF  (75.68 MB)
5  SARIMA  (16.02 MB)
6  Grid search to select the best parameters  (30.09 MB)
7  Exam ARIMA  (36.73 MB)
1  Error calculation and interpretation  (48.73 MB)
2  Different formulas to calculate error  (16.19 MB)
3  Train test split  (49.87 MB)
4  Model comparison  (28.39 MB)
1  Introduction  (23.66 MB)
2  Create Python environment for TensorFlow  (30.59 MB)
3  Preprocess time series  (52.77 MB)
4  Input dimension  (17.29 MB)
5  Early stopping to save computation  (42.17 MB)
6  Evaluation actual vs predicted data  (39.04 MB)
7  Interpretation RMSE and MAE  (23.02 MB)

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