| 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. 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 | ||
| 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 | ||
| 1. How does stationarity look in a time series.mp4 | 3 MB | ||
| 1. How does stationarity look in a time series.srt | 1.5 KB | ||
| 1. Introducing seasonal order with SARIMA model.mp4 | 5.8 MB | ||
| 1. Introducing seasonal order with SARIMA model.srt | 2 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 | ||
| 1. Introduction to developing ARIMA models.mp4 | 7.4 MB | ||
| 1. Introduction to developing ARIMA models.srt | 3 KB | ||
| 1. Intuition behind forecasting models.mp4 | 4.8 MB | ||
| 1. Intuition behind forecasting models.srt | 2.6 KB | ||
| 1. Methods to visualize data with Python.mp4 | 7.8 MB | ||
| 1. Methods to visualize data with Python.srt | 3.2 KB | ||
| 1. Next steps.mp4 | 3.4 MB | ||
| 1. Next steps.srt | 1.6 KB | ||
| 1. SARIMA vs. exponential smoothing.mp4 | 3.5 MB | ||
| 1. SARIMA vs. exponential smoothing.srt | 1.9 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 | ||
| 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 | ||
| 1. Why learn practical Python for time series forecasting.mp4 | 3.8 MB | ||
| 1. Why learn practical Python for time series forecasting.srt | 1 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 | ||
| 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. Build DataFrame to gather forecasted future values.mp4 | 16.7 MB | ||
| 2. Build DataFrame to gather forecasted future values.srt | 7.7 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 | ||
| 2. Data preprocessing for insightful decomposition.mp4 | 15 MB | ||
| 2. Data preprocessing for insightful decomposition.srt | 6.7 KB | ||
| 2. Error metrics and steps to calculate.mp4 | 15.8 MB | ||
| 2. Error metrics and steps to calculate.srt | 6.9 KB | ||
| 2. Fit mathematical equation model.mp4 | 12.4 MB | ||
| 2. Fit mathematical equation model.srt | 5.5 KB | ||
| 2. How to use Codespaces.mp4 | 9.2 MB | ||
| 2. How to use Codespaces.srt | 4.6 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 | ||
| 2. Log transformation to achieve data stationarity.mp4 | 10.4 MB | ||
| 2. Log transformation to achieve data stationarity.srt | 4.8 KB | ||
| 2. Model fit and forecast.mp4 | 11.3 MB | ||
| 2. Model fit and forecast.srt | 5.1 KB | ||
| 2. Model fit step by step.mp4 | 16.8 MB | ||
| 2. Model fit step by step.srt | 7.3 KB | ||
| 2. Python libraries for data visualization.mp4 | 10.7 MB | ||
| 2. Python libraries for data visualization.srt | 6.3 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 | ||
| 2. Train-test split for one model.mp4 | 22.7 MB | ||
| 2. Train-test split for one model.srt | 10.7 KB | ||
| 3. Datetime components on different columns.mp4 | 2.4 MB | ||
| 3. Datetime components on different columns.srt | 1.4 KB | ||
| 3. Diagnostics to validate assumptions.mp4 | 5.6 MB | ||
| 3. Diagnostics to validate assumptions.srt | 3.2 KB | ||
| 3. Evaluate multiple models at once.mp4 | 25.7 MB | ||
| 3. Evaluate multiple models at once.srt | 9.7 KB | ||
| 3. Feed holidays data into the model.mp4 | 5.8 MB | ||
| 3. Feed holidays data into the model.srt | 2.4 KB | ||
| 3. How ARIMA changes with parameters P, D, and Q.mp4 | 5 MB | ||
| 3. How ARIMA changes with parameters P, D, and Q.srt | 2.1 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 | ||
| 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 | ||
| 3. Interpretation of metrics in business terms.mp4 | 7.5 MB | ||
| 3. Interpretation of metrics in business terms.srt | 4.2 KB | ||
| 3. Moving average method.mp4 | 16.9 MB | ||
| 3. Moving average method.srt | 7.6 KB | ||
| 3. Reverse log transformation on forecasted data.mp4 | 7.4 MB | ||
| 3. Reverse log transformation on forecasted data.srt | 3.7 KB | ||
| 3. Seasonal decompose with Statsmodels.mp4 | 8.9 MB | ||
| 3. Seasonal decompose with Statsmodels.srt | 4.4 KB | ||
| 3. Set Plotly as pandas backend for plotting.mp4 | 4 MB | ||
| 3. Set Plotly as pandas backend for plotting.srt | 2 KB | ||
| 3. Understand model configurations based on playground.mp4 | 8.4 MB | ||
| 3. Understand model configurations based on playground.srt | 3.8 KB | ||
| 4. Customize default Plotly theme.mp4 | 10.6 MB | ||
| 4. Customize default Plotly theme.srt | 5.1 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 | ||
| 4. Data transformations to achieve stationarity.mp4 | 6.2 MB | ||
| 4. Data transformations to achieve stationarity.srt | 3.1 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 | ||
| 4. Differencing to achieve stationarity.mp4 | 13.5 MB | ||
| 4. Differencing to achieve stationarity.srt | 6.3 KB | ||
| 4. Interpret decomposition models Additive vs. multiplicative.mp4 | 10.8 MB | ||
| 4. Interpret decomposition models Additive vs. multiplicative.srt | 5.3 KB | ||
| 4. Seasonal naive method.mp4 | 6.1 MB | ||
| 4. Seasonal naive method.srt | 3 KB | ||
| 4. Summary From ARIMA to SARIMA.mp4 | 6.9 MB | ||
| 4. Summary From ARIMA to SARIMA.srt | 2.9 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 | ||
| 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. ACF and PACF.mp4 | 18.2 MB | ||
| 5. ACF and PACF.srt | 8.4 KB | ||
| 5. Build DataFrame of components.mp4 | 13.9 MB | ||
| 5. Build DataFrame of components.srt | 5.5 KB | ||
| 5. Configure seasonality parameters in Prophet.mp4 | 5.9 MB | ||
| 5. Configure seasonality parameters in Prophet.srt | 2.8 KB | ||
| 5. How to interpret different plot types.mp4 | 8.5 MB | ||
| 5. How to interpret different plot types.srt | 4.2 KB | ||
| 5. Load and preprocess data from Excel.mp4 | 5.6 MB | ||
| 5. Load and preprocess data from Excel.srt | 3.4 KB | ||
| 6. Compare models using Plotly interactive visualization.mp4 | 15.9 MB | ||
| 6. Compare models using Plotly interactive visualization.srt | 6.3 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 | ||
| 6. Playground to try different configurations.mp4 | 16.9 MB | ||
| 6. Playground to try different configurations.srt | 6 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 | ||
| 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 | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 132 total files | |||
Python for Time Series Forecasting (2025)
https://WebToolTip.com
Released 07/2025
With Jesus Lopez
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 4h 19m 11s | Size: 750 MB
Master time series forecasting in Python using real datasets, with hands-on skills in preprocessing, visualization, decomposition, model selection, and diagnostics.
Course details
Learn practical time series forecasting with Python using real-world datasets from energy (EIA – U.S. Energy Information Administration) and economics (FRED – Federal Reserve Economic Data).
Build skills step by step, from loading and preprocessing time series data to decomposing trends and seasonality, visualizing patterns with Plotly, and applying forecasting models like ARIMA, SARIMA, exponential smoothing, and Prophet. Learn to evaluate model performance using error metrics and cross-validation techniques like walk-forward validation.
The course emphasizes hands-on exercises in a GitHub Codespaces environment, so you can immediately apply what you learn to your own datasets. Whether you’re working with sales, energy, or financial data, you’ll gain the skills to generate accurate, interpretable forecasts that drive real-world decisions.
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| 234.3 MB | SeedHash | 1 week | 23 | 7 | |
| 1.8 GB | freecoursewb | 3 weeks | 49 | 5 | |
| 2.2 GB | freecoursewb | 3 weeks | 34 | 5 | |
| 1 GB | freecoursewb | 1 month | 0 | 0 | |
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Udemy - Python Programming for Beginners - Learn Python from Scratch Posted by
freecoursewb in Other
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3.9 GB | freecoursewb | 1 month | 41 | 6 |
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