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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