Python for Time Series Forecasting 2025
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| Torrent Hash : | 3C6D3ADE523F621ABA2F35FA894753C5763A4468 |
| Torrent Added : | at July 31, 2025, 12:43 a.m. in Other |
| Torrent Size : | 750.8 MB |
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Python for Time Series Forecasting 2025
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1. Why learn practical Python for time series forecasting.mp4 -
1. Why learn practical Python for time series forecasting.srt -
2. How to use Codespaces.mp4 -
2. How to use Codespaces.srt -
1. Search and download Federal Reserve Economic Data.mp4 -
1. Search and download Federal Reserve Economic Data.srt -
2. Load CSV and set dtype as datetime.mp4 -
2. Load CSV and set dtype as datetime.srt -
3. Datetime components on different columns.mp4 -
3. Datetime components on different columns.srt -
4. Why set the datetime column as index.mp4 -
4. Why set the datetime column as index.srt -
5. Load and preprocess data from Excel.mp4 -
5. Load and preprocess data from Excel.srt -
1. Configure a template notebook based on new datasets.mp4 -
1. Configure a template notebook based on new datasets.srt -
1. SARIMA vs. exponential smoothing.mp4 -
1. SARIMA vs. exponential smoothing.srt -
2. Model fit and forecast.mp4 -
2. Model fit and forecast.srt -
3. Understand model configurations based on playground.mp4 -
3. Understand model configurations based on playground.srt -
4. Diagnostics to validate assumptions and inform model choice.mp4 -
4. Diagnostics to validate assumptions and inform model choice.srt -
1. Introduction to Prophet A semi-automatic time series model.mp4 -
1. Introduction to Prophet A semi-automatic time series model.srt -
2. Model fit step by step.mp4 -
2. Model fit step by step.srt -
3. Feed holidays data into the model.mp4 -
3. Feed holidays data into the model.srt -
4. Data preprocessing to forecast and visualize values.mp4 -
4. Data preprocessing to forecast and visualize values.srt -
5. Configure seasonality parameters in Prophet.mp4 -
5. Configure seasonality parameters in Prophet.srt -
6. How to interpret diagnostics with robust models.mp4 -
6. How to interpret diagnostics with robust models.srt -
1. Why test on unseen data during model fit.mp4 -
1. Why test on unseen data during model fit.srt -
2. Train-test split for one model.mp4 -
2. Train-test split for one model.srt -
3. Evaluate multiple models at once.mp4 -
3. Evaluate multiple models at once.srt -
1. Configure a template notebook based on new datasets.mp4 -
1. Configure a template notebook based on new datasets.srt -
1. Walk-forward validation as a more realistic choice.mp4 -
1. Walk-forward validation as a more realistic choice.srt -
2. Run a walk-forward experiment with multiple models.mp4 -
2. Run a walk-forward experiment with multiple models.srt -
3. How does TimeSeriesSplit work to produce walk-forward sets.mp4 -
3. How does TimeSeriesSplit work to produce walk-forward sets.srt -
1. Next steps.mp4 -
1. Next steps.srt -
1. Methods to visualize data with Python.mp4 -
1. Methods to visualize data with Python.srt -
2. Python libraries for data visualization.mp4 -
2. Python libraries for data visualization.srt -
3. Set Plotly as pandas backend for plotting.mp4 -
3. Set Plotly as pandas backend for plotting.srt -
4. Customize default Plotly theme.mp4 -
4. Customize default Plotly theme.srt -
5. How to interpret different plot types.mp4 -
5. How to interpret different plot types.srt -
6. Tricks to visualize multiple time series at once.mp4 -
6. Tricks to visualize multiple time series at once.srt -
1. Decomposing California solar energy using data from EIA.mp4 -
1. Decomposing California solar energy using data from EIA.srt -
2. Data preprocessing for insightful decomposition.mp4 -
2. Data preprocessing for insightful decomposition.srt -
3. Seasonal decompose with Statsmodels.mp4 -
3. Seasonal decompose with Statsmodels.srt -
4. Interpret decomposition models Additive vs. multiplicative.mp4 -
4. Interpret decomposition models Additive vs. multiplicative.srt -
5. Build DataFrame of components.mp4 -
5. Build DataFrame of components.srt -
6. Compare models using Plotly interactive visualization.mp4 -
6. Compare models using Plotly interactive visualization.srt -
1. Download US energy data using Python with EIA API.mp4 -
1. Download US energy data using Python with EIA API.srt -
2. Configure a template notebook based on new datasets.mp4 -
2. Configure a template notebook based on new datasets.srt -
3. How to specify the aggregation rule and periods.mp4 -
3. How to specify the aggregation rule and periods.srt -
4. Using Copilot to interpret a visual report with AI.mp4 -
4. Using Copilot to interpret a visual report with AI.srt -
1. Intuition behind forecasting models.mp4 -
1. Intuition behind forecasting models.srt -
2. Build DataFrame to gather forecasted future values.mp4 -
2. Build DataFrame to gather forecasted future values.srt -
3. Moving average method.mp4 -
3. Moving average method.srt -
4. Seasonal naive method.mp4 -
4. Seasonal naive method.srt -
1. Introduction to developing ARIMA models.mp4 -
1. Introduction to developing ARIMA models.srt -
2. Fit mathematical equation model.mp4 -
2. Fit mathematical equation model.srt -
3. How ARIMA changes with parameters P, D, and Q.mp4 -
3. How ARIMA changes with parameters P, D, and Q.srt -
4. Differencing to achieve stationarity.mp4 -
4. Differencing to achieve stationarity.srt -
5. ACF and PACF.mp4 -
5. ACF and PACF.srt -
6. Playground to try different configurations.mp4 -
6. Playground to try different configurations.srt -
7. Diagnostics to validate assumptions.mp4 -
7. Diagnostics to validate assumptions.srt -
8. Summary Important steps to consider in ARIMA modeling.mp4 -
8. Summary Important steps to consider in ARIMA modeling.srt -
1. Introducing seasonal order with SARIMA model.mp4 -
1. Introducing seasonal order with SARIMA model.srt -
2. Model fit and forecast.mp4 -
2. Model fit and forecast.srt -
3. Diagnostics to validate assumptions.mp4 -
3. Diagnostics to validate assumptions.srt -
4. Summary From ARIMA to SARIMA.mp4 -
4. Summary From ARIMA to SARIMA.srt -
1. How does stationarity look in a time series.mp4 -
1. How does stationarity look in a time series.srt -
2. Log transformation to achieve data stationarity.mp4 -
2. Log transformation to achieve data stationarity.srt -
3. Reverse log transformation on forecasted data.mp4 -
3. Reverse log transformation on forecasted data.srt -
4. Data transformations to achieve stationarity.mp4 -
4. Data transformations to achieve stationarity.srt -
1. Why use a metric that aggregates the residuals of a model.mp4 -
1. Why use a metric that aggregates the residuals of a model.srt -
2. Error metrics and steps to calculate.mp4 -
2. Error metrics and steps to calculate.srt -
3. Interpretation of metrics in business terms.mp4 -
3. Interpretation of metrics in business terms.srt -
Bonus Resources.txt -
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Get Bonus Downloads Here.url -
180 bytes
1. Why learn practical Python for time series forecasting.mp4 -
3.8 MB
1. Why learn practical Python for time series forecasting.srt -
1.0 KB
2. How to use Codespaces.mp4 -
9.2 MB
2. How to use Codespaces.srt -
4.6 KB
1. Search and download Federal Reserve Economic Data.mp4 -
4.5 MB
1. Search and download Federal Reserve Economic Data.srt -
1.9 KB
2. Load CSV and set dtype as datetime.mp4 -
12.6 MB
2. Load CSV and set dtype as datetime.srt -
6.8 KB
3. Datetime components on different columns.mp4 -
2.4 MB
3. Datetime components on different columns.srt -
1.4 KB
4. Why set the datetime column as index.mp4 -
8.4 MB
4. Why set the datetime column as index.srt -
4.9 KB
5. Load and preprocess data from Excel.mp4 -
5.6 MB
5. Load and preprocess data from Excel.srt -
3.4 KB
1. Configure a template notebook based on new datasets.mp4 -
39.8 MB
1. Configure a template notebook based on new datasets.srt -
16.6 KB
1. SARIMA vs. exponential smoothing.mp4 -
3.5 MB
1. SARIMA vs. exponential smoothing.srt -
1.9 KB
2. Model fit and forecast.mp4 -
7.2 MB
2. Model fit and forecast.srt -
3.0 KB
3. Understand model configurations based on playground.mp4 -
8.4 MB
3. Understand model configurations based on playground.srt -
3.8 KB
4. Diagnostics to validate assumptions and inform model choice.mp4 -
7.7 MB
4. Diagnostics to validate assumptions and inform model choice.srt -
3.6 KB
1. Introduction to Prophet A semi-automatic time series model.mp4 -
6.7 MB
1. Introduction to Prophet A semi-automatic time series model.srt -
2.8 KB
2. Model fit step by step.mp4 -
16.8 MB
2. Model fit step by step.srt -
7.3 KB
3. Feed holidays data into the model.mp4 -
5.8 MB
3. Feed holidays data into the model.srt -
2.4 KB
4. Data preprocessing to forecast and visualize values.mp4 -
6.4 MB
4. Data preprocessing to forecast and visualize values.srt -
2.9 KB
5. Configure seasonality parameters in Prophet.mp4 -
5.9 MB
5. Configure seasonality parameters in Prophet.srt -
2.8 KB
6. How to interpret diagnostics with robust models.mp4 -
3.9 MB
6. How to interpret diagnostics with robust models.srt -
1.9 KB
1. Why test on unseen data during model fit.mp4 -
13.6 MB
1. Why test on unseen data during model fit.srt -
6.4 KB
2. Train-test split for one model.mp4 -
22.7 MB
2. Train-test split for one model.srt -
10.7 KB
3. Evaluate multiple models at once.mp4 -
25.7 MB
3. Evaluate multiple models at once.srt -
9.7 KB
1. Configure a template notebook based on new datasets.mp4 -
40.4 MB
1. Configure a template notebook based on new datasets.srt -
14.3 KB
1. Walk-forward validation as a more realistic choice.mp4 -
7.1 MB
1. Walk-forward validation as a more realistic choice.srt -
2.9 KB
2. Run a walk-forward experiment with multiple models.mp4 -
26.6 MB
2. Run a walk-forward experiment with multiple models.srt -
10.1 KB
3. How does TimeSeriesSplit work to produce walk-forward sets.mp4 -
13.1 MB
3. How does TimeSeriesSplit work to produce walk-forward sets.srt -
5.8 KB
1. Next steps.mp4 -
3.4 MB
1. Next steps.srt -
1.6 KB
1. Methods to visualize data with Python.mp4 -
7.8 MB
1. Methods to visualize data with Python.srt -
3.2 KB
2. Python libraries for data visualization.mp4 -
10.7 MB
2. Python libraries for data visualization.srt -
6.3 KB
3. Set Plotly as pandas backend for plotting.mp4 -
4.0 MB
3. Set Plotly as pandas backend for plotting.srt -
2.0 KB
4. Customize default Plotly theme.mp4 -
10.6 MB
4. Customize default Plotly theme.srt -
5.1 KB
5. How to interpret different plot types.mp4 -
8.5 MB
5. How to interpret different plot types.srt -
4.2 KB
6. Tricks to visualize multiple time series at once.mp4 -
7.9 MB
6. Tricks to visualize multiple time series at once.srt -
4.1 KB
1. Decomposing California solar energy using data from EIA.mp4 -
6.9 MB
1. Decomposing California solar energy using data from EIA.srt -
2.9 KB
2. Data preprocessing for insightful decomposition.mp4 -
15.0 MB
2. Data preprocessing for insightful decomposition.srt -
6.7 KB
3. Seasonal decompose with Statsmodels.mp4 -
8.9 MB
3. Seasonal decompose with Statsmodels.srt -
4.4 KB
4. Interpret decomposition models Additive vs. multiplicative.mp4 -
10.8 MB
4. Interpret decomposition models Additive vs. multiplicative.srt -
5.3 KB
5. Build DataFrame of components.mp4 -
13.9 MB
5. Build DataFrame of components.srt -
5.5 KB
6. Compare models using Plotly interactive visualization.mp4 -
15.9 MB
6. Compare models using Plotly interactive visualization.srt -
6.3 KB
1. Download US energy data using Python with EIA API.mp4 -
27.1 MB
1. Download US energy data using Python with EIA API.srt -
9.2 KB
2. Configure a template notebook based on new datasets.mp4 -
36.6 MB
2. Configure a template notebook based on new datasets.srt -
13.1 KB
3. How to specify the aggregation rule and periods.mp4 -
8.2 MB
3. How to specify the aggregation rule and periods.srt -
3.2 KB
4. Using Copilot to interpret a visual report with AI.mp4 -
8.9 MB
4. Using Copilot to interpret a visual report with AI.srt -
3.2 KB
1. Intuition behind forecasting models.mp4 -
4.8 MB
1. Intuition behind forecasting models.srt -
2.6 KB
2. Build DataFrame to gather forecasted future values.mp4 -
16.7 MB
2. Build DataFrame to gather forecasted future values.srt -
7.7 KB
3. Moving average method.mp4 -
16.9 MB
3. Moving average method.srt -
7.6 KB
4. Seasonal naive method.mp4 -
6.1 MB
4. Seasonal naive method.srt -
3.0 KB
1. Introduction to developing ARIMA models.mp4 -
7.4 MB
1. Introduction to developing ARIMA models.srt -
3.0 KB
2. Fit mathematical equation model.mp4 -
12.4 MB
2. Fit mathematical equation model.srt -
5.5 KB
3. How ARIMA changes with parameters P, D, and Q.mp4 -
5.0 MB
3. How ARIMA changes with parameters P, D, and Q.srt -
2.1 KB
4. Differencing to achieve stationarity.mp4 -
13.5 MB
4. Differencing to achieve stationarity.srt -
6.3 KB
5. ACF and PACF.mp4 -
18.2 MB
5. ACF and PACF.srt -
8.4 KB
6. Playground to try different configurations.mp4 -
16.9 MB
6. Playground to try different configurations.srt -
6.0 KB
7. Diagnostics to validate assumptions.mp4 -
24.5 MB
7. Diagnostics to validate assumptions.srt -
11.4 KB
8. Summary Important steps to consider in ARIMA modeling.mp4 -
7.4 MB
8. Summary Important steps to consider in ARIMA modeling.srt -
3.8 KB
1. Introducing seasonal order with SARIMA model.mp4 -
5.8 MB
1. Introducing seasonal order with SARIMA model.srt -
2.0 KB
2. Model fit and forecast.mp4 -
11.3 MB
2. Model fit and forecast.srt -
5.1 KB
3. Diagnostics to validate assumptions.mp4 -
5.6 MB
3. Diagnostics to validate assumptions.srt -
3.2 KB
4. Summary From ARIMA to SARIMA.mp4 -
6.9 MB
4. Summary From ARIMA to SARIMA.srt -
2.9 KB
1. How does stationarity look in a time series.mp4 -
3.0 MB
1. How does stationarity look in a time series.srt -
1.5 KB
2. Log transformation to achieve data stationarity.mp4 -
10.4 MB
2. Log transformation to achieve data stationarity.srt -
4.8 KB
3. Reverse log transformation on forecasted data.mp4 -
7.4 MB
3. Reverse log transformation on forecasted data.srt -
3.7 KB
4. Data transformations to achieve stationarity.mp4 -
6.2 MB
4. Data transformations to achieve stationarity.srt -
3.1 KB
1. Why use a metric that aggregates the residuals of a model.mp4 -
7.7 MB
1. Why use a metric that aggregates the residuals of a model.srt -
3.1 KB
2. Error metrics and steps to calculate.mp4 -
15.8 MB
2. Error metrics and steps to calculate.srt -
6.9 KB
3. Interpretation of metrics in business terms.mp4 -
7.5 MB
3. Interpretation of metrics in business terms.srt -
4.2 KB
Bonus Resources.txt -
70 bytes
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