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Language:
English
Total Size:
750.8 MB
Info Hash:
3C6D3ADE523F621ABA2F35FA894753C5763A4468
Added By:
Added:
July 31, 2025, 12:43 a.m.
Stats:
|
(Last updated: July 31, 2025, 12:48 a.m.)
| File | Size |
|---|---|
| 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 |
Name
DL
Uploader
Size
S/L
Added
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374.8 MB
[0
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5]
2023-10-24
| Uploaded by freecoursewb | Size 374.8 MB | Health [ 0 /5 ] | Added 2023-10-24 |
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273.2 MB
[8
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5]
2023-07-01
| Uploaded by FreeCourseWeb | Size 273.2 MB | Health [ 8 /5 ] | Added 2023-07-01 |
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750.8 MB
[1
/
4]
2025-07-31
| Uploaded by freecoursewb | Size 750.8 MB | Health [ 1 /4 ] | Added 2025-07-31 |
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345.2 MB
[4
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1]
2023-06-01
| Uploaded by freecoursewb | Size 345.2 MB | Health [ 4 /1 ] | Added 2023-06-01 |
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973.7 MB
[0
/
0]
2023-10-30
| Uploaded by freecoursewb | Size 973.7 MB | Health [ 0 /0 ] | Added 2023-10-30 |
-
571.8 MB
[0
/
0]
2023-07-02
| Uploaded by FreeCourseWeb | Size 571.8 MB | Health [ 0 /0 ] | Added 2023-07-02 |
NOTE
SOURCE: Python for Time Series Forecasting 2025
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