Python for Time Series Forecasting (2025)

Code:

• Get Bonus Downloads Here.url (0.2 KB)
• 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 (0.1 KB)