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[ WebToolTip.com ] Python for Time Series Forecasting (2025)
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[ WebToolTip.com ] Python for Time Series Forecasting (2025)
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文件列表
~Get Your Files Here !/14 - Assignment 3/1. Configure a template notebook based on new datasets.mp4
42.4 MB
~Get Your Files Here !/10 - Assignment 2/1. Configure a template notebook based on new datasets.mp4
41.7 MB
~Get Your Files Here !/4 - Assignment 1/2. Configure a template notebook based on new datasets.mp4
38.4 MB
~Get Your Files Here !/4 - Assignment 1/1. Download US energy data using Python with EIA API.mp4
28.4 MB
~Get Your Files Here !/15 - Walk-Forward Validation/2. Run a walk-forward experiment with multiple models.mp4
27.9 MB
~Get Your Files Here !/13 - Evaluate and Compare Time Series Models Train Test Split/3. Evaluate multiple models at once.mp4
27.0 MB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/7. Diagnostics to validate assumptions.mp4
25.7 MB
~Get Your Files Here !/13 - Evaluate and Compare Time Series Models Train Test Split/2. Train-test split for one model.mp4
23.9 MB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/5. ACF and PACF.mp4
19.1 MB
~Get Your Files Here !/5 - Model Time Series to Forecast Baseline Models/3. Moving average method.mp4
17.8 MB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/6. Playground to try different configurations.mp4
17.7 MB
~Get Your Files Here !/12 - Prophet Modeling/2. Model fit step by step.mp4
17.6 MB
~Get Your Files Here !/5 - Model Time Series to Forecast Baseline Models/2. Build DataFrame to gather forecasted future values.mp4
17.5 MB
~Get Your Files Here !/3 - Time Series Decomposition/6. Compare models using Plotly interactive visualization.mp4
16.7 MB
~Get Your Files Here !/9 - Metrics to Measure Model Performance/2. Error metrics and steps to calculate.mp4
16.6 MB
~Get Your Files Here !/3 - Time Series Decomposition/2. Data preprocessing for insightful decomposition.mp4
15.7 MB
~Get Your Files Here !/3 - Time Series Decomposition/5. Build DataFrame of components.mp4
14.6 MB
~Get Your Files Here !/13 - Evaluate and Compare Time Series Models Train Test Split/1. Why test on unseen data during model fit.mp4
14.3 MB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/4. Differencing to achieve stationarity.mp4
14.2 MB
~Get Your Files Here !/15 - Walk-Forward Validation/3. How does TimeSeriesSplit work to produce walk-forward sets.mp4
13.7 MB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/2. Load CSV and set dtype as datetime.mp4
13.2 MB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/2. Fit mathematical equation model.mp4
13.0 MB
~Get Your Files Here !/7 - Seasonal Autoregressive Integrated Moving Average (SARIMA)/2. Model fit and forecast.mp4
11.8 MB
~Get Your Files Here !/3 - Time Series Decomposition/4. Interpret decomposition models Additive vs. multiplicative.mp4
11.3 MB
~Get Your Files Here !/2 - Visualize Time Series Data/2. Python libraries for data visualization.mp4
11.2 MB
~Get Your Files Here !/2 - Visualize Time Series Data/4. Customize default Plotly theme.mp4
11.1 MB
~Get Your Files Here !/8 - Data Stationarity/2. Log transformation to achieve data stationarity.mp4
10.9 MB
~Get Your Files Here !/0 - Introduction/2. How to use Codespaces.mp4
9.7 MB
~Get Your Files Here !/3 - Time Series Decomposition/3. Seasonal decompose with Statsmodels.mp4
9.4 MB
~Get Your Files Here !/4 - Assignment 1/4. Using Copilot to interpret a visual report with AI.mp4
9.4 MB
~Get Your Files Here !/2 - Visualize Time Series Data/5. How to interpret different plot types.mp4
8.9 MB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/4. Why set the datetime column as index.mp4
8.8 MB
~Get Your Files Here !/11 - Exponential Smoothing Models/3. Understand model configurations based on playground.mp4
8.8 MB
~Get Your Files Here !/4 - Assignment 1/3. How to specify the aggregation rule and periods.mp4
8.6 MB
~Get Your Files Here !/2 - Visualize Time Series Data/6. Tricks to visualize multiple time series at once.mp4
8.3 MB
~Get Your Files Here !/2 - Visualize Time Series Data/1. Methods to visualize data with Python.mp4
8.2 MB
~Get Your Files Here !/9 - Metrics to Measure Model Performance/1. Why use a metric that aggregates the residuals of a model.mp4
8.1 MB
~Get Your Files Here !/11 - Exponential Smoothing Models/4. Diagnostics to validate assumptions and inform model choice.mp4
8.0 MB
~Get Your Files Here !/9 - Metrics to Measure Model Performance/3. Interpretation of metrics in business terms.mp4
7.8 MB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/1. Introduction to developing ARIMA models.mp4
7.8 MB
~Get Your Files Here !/8 - Data Stationarity/3. Reverse log transformation on forecasted data.mp4
7.8 MB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/8. Summary Important steps to consider in ARIMA modeling.mp4
7.7 MB
~Get Your Files Here !/11 - Exponential Smoothing Models/2. Model fit and forecast.mp4
7.5 MB
~Get Your Files Here !/15 - Walk-Forward Validation/1. Walk-forward validation as a more realistic choice.mp4
7.4 MB
~Get Your Files Here !/7 - Seasonal Autoregressive Integrated Moving Average (SARIMA)/4. Summary From ARIMA to SARIMA.mp4
7.3 MB
~Get Your Files Here !/3 - Time Series Decomposition/1. Decomposing California solar energy using data from EIA.mp4
7.2 MB
~Get Your Files Here !/12 - Prophet Modeling/1. Introduction to Prophet A semi-automatic time series model.mp4
7.0 MB
~Get Your Files Here !/12 - Prophet Modeling/4. Data preprocessing to forecast and visualize values.mp4
6.8 MB
~Get Your Files Here !/8 - Data Stationarity/4. Data transformations to achieve stationarity.mp4
6.5 MB
~Get Your Files Here !/5 - Model Time Series to Forecast Baseline Models/4. Seasonal naive method.mp4
6.4 MB
~Get Your Files Here !/12 - Prophet Modeling/5. Configure seasonality parameters in Prophet.mp4
6.2 MB
~Get Your Files Here !/7 - Seasonal Autoregressive Integrated Moving Average (SARIMA)/1. Introducing seasonal order with SARIMA model.mp4
6.1 MB
~Get Your Files Here !/12 - Prophet Modeling/3. Feed holidays data into the model.mp4
6.1 MB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/5. Load and preprocess data from Excel.mp4
5.9 MB
~Get Your Files Here !/7 - Seasonal Autoregressive Integrated Moving Average (SARIMA)/3. Diagnostics to validate assumptions.mp4
5.8 MB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/3. How ARIMA changes with parameters P, D, and Q.mp4
5.3 MB
~Get Your Files Here !/5 - Model Time Series to Forecast Baseline Models/1. Intuition behind forecasting models.mp4
5.0 MB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/1. Search and download Federal Reserve Economic Data.mp4
4.7 MB
~Get Your Files Here !/2 - Visualize Time Series Data/3. Set Plotly as pandas backend for plotting.mp4
4.2 MB
~Get Your Files Here !/12 - Prophet Modeling/6. How to interpret diagnostics with robust models.mp4
4.0 MB
~Get Your Files Here !/0 - Introduction/1. Why learn practical Python for time series forecasting.mp4
4.0 MB
~Get Your Files Here !/11 - Exponential Smoothing Models/1. SARIMA vs. exponential smoothing.mp4
3.7 MB
~Get Your Files Here !/16 - Conclusion/1. Next steps.mp4
3.5 MB
~Get Your Files Here !/8 - Data Stationarity/1. How does stationarity look in a time series.mp4
3.1 MB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/3. Datetime components on different columns.mp4
2.5 MB
~Get Your Files Here !/10 - Assignment 2/1. Configure a template notebook based on new datasets.srt
17.0 kB
~Get Your Files Here !/14 - Assignment 3/1. Configure a template notebook based on new datasets.srt
14.7 kB
~Get Your Files Here !/4 - Assignment 1/2. Configure a template notebook based on new datasets.srt
13.5 kB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/7. Diagnostics to validate assumptions.srt
11.6 kB
~Get Your Files Here !/13 - Evaluate and Compare Time Series Models Train Test Split/2. Train-test split for one model.srt
10.9 kB
~Get Your Files Here !/15 - Walk-Forward Validation/2. Run a walk-forward experiment with multiple models.srt
10.3 kB
~Get Your Files Here !/13 - Evaluate and Compare Time Series Models Train Test Split/3. Evaluate multiple models at once.srt
9.9 kB
~Get Your Files Here !/4 - Assignment 1/1. Download US energy data using Python with EIA API.srt
9.4 kB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/5. ACF and PACF.srt
8.6 kB
~Get Your Files Here !/5 - Model Time Series to Forecast Baseline Models/2. Build DataFrame to gather forecasted future values.srt
7.9 kB
~Get Your Files Here !/5 - Model Time Series to Forecast Baseline Models/3. Moving average method.srt
7.8 kB
~Get Your Files Here !/12 - Prophet Modeling/2. Model fit step by step.srt
7.4 kB
~Get Your Files Here !/9 - Metrics to Measure Model Performance/2. Error metrics and steps to calculate.srt
7.0 kB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/2. Load CSV and set dtype as datetime.srt
7.0 kB
~Get Your Files Here !/3 - Time Series Decomposition/2. Data preprocessing for insightful decomposition.srt
6.9 kB
~Get Your Files Here !/13 - Evaluate and Compare Time Series Models Train Test Split/1. Why test on unseen data during model fit.srt
6.5 kB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/4. Differencing to achieve stationarity.srt
6.5 kB
~Get Your Files Here !/2 - Visualize Time Series Data/2. Python libraries for data visualization.srt
6.4 kB
~Get Your Files Here !/3 - Time Series Decomposition/6. Compare models using Plotly interactive visualization.srt
6.4 kB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/6. Playground to try different configurations.srt
6.1 kB
~Get Your Files Here !/15 - Walk-Forward Validation/3. How does TimeSeriesSplit work to produce walk-forward sets.srt
5.9 kB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/2. Fit mathematical equation model.srt
5.6 kB
~Get Your Files Here !/3 - Time Series Decomposition/5. Build DataFrame of components.srt
5.6 kB
~Get Your Files Here !/3 - Time Series Decomposition/4. Interpret decomposition models Additive vs. multiplicative.srt
5.4 kB
~Get Your Files Here !/7 - Seasonal Autoregressive Integrated Moving Average (SARIMA)/2. Model fit and forecast.srt
5.2 kB
~Get Your Files Here !/2 - Visualize Time Series Data/4. Customize default Plotly theme.srt
5.2 kB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/4. Why set the datetime column as index.srt
5.0 kB
~Get Your Files Here !/8 - Data Stationarity/2. Log transformation to achieve data stationarity.srt
4.9 kB
~Get Your Files Here !/0 - Introduction/2. How to use Codespaces.srt
4.7 kB
~Get Your Files Here !/3 - Time Series Decomposition/3. Seasonal decompose with Statsmodels.srt
4.5 kB
~Get Your Files Here !/2 - Visualize Time Series Data/5. How to interpret different plot types.srt
4.3 kB
~Get Your Files Here !/9 - Metrics to Measure Model Performance/3. Interpretation of metrics in business terms.srt
4.3 kB
~Get Your Files Here !/2 - Visualize Time Series Data/6. Tricks to visualize multiple time series at once.srt
4.2 kB
~Get Your Files Here !/11 - Exponential Smoothing Models/3. Understand model configurations based on playground.srt
3.9 kB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/8. Summary Important steps to consider in ARIMA modeling.srt
3.9 kB
~Get Your Files Here !/8 - Data Stationarity/3. Reverse log transformation on forecasted data.srt
3.8 kB
~Get Your Files Here !/11 - Exponential Smoothing Models/4. Diagnostics to validate assumptions and inform model choice.srt
3.7 kB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/5. Load and preprocess data from Excel.srt
3.4 kB
~Get Your Files Here !/7 - Seasonal Autoregressive Integrated Moving Average (SARIMA)/3. Diagnostics to validate assumptions.srt
3.3 kB
~Get Your Files Here !/4 - Assignment 1/4. Using Copilot to interpret a visual report with AI.srt
3.3 kB
~Get Your Files Here !/4 - Assignment 1/3. How to specify the aggregation rule and periods.srt
3.3 kB
~Get Your Files Here !/2 - Visualize Time Series Data/1. Methods to visualize data with Python.srt
3.2 kB
~Get Your Files Here !/9 - Metrics to Measure Model Performance/1. Why use a metric that aggregates the residuals of a model.srt
3.2 kB
~Get Your Files Here !/8 - Data Stationarity/4. Data transformations to achieve stationarity.srt
3.2 kB
~Get Your Files Here !/5 - Model Time Series to Forecast Baseline Models/4. Seasonal naive method.srt
3.1 kB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/1. Introduction to developing ARIMA models.srt
3.1 kB
~Get Your Files Here !/11 - Exponential Smoothing Models/2. Model fit and forecast.srt
3.0 kB
~Get Your Files Here !/12 - Prophet Modeling/4. Data preprocessing to forecast and visualize values.srt
3.0 kB
~Get Your Files Here !/7 - Seasonal Autoregressive Integrated Moving Average (SARIMA)/4. Summary From ARIMA to SARIMA.srt
3.0 kB
~Get Your Files Here !/3 - Time Series Decomposition/1. Decomposing California solar energy using data from EIA.srt
3.0 kB
~Get Your Files Here !/15 - Walk-Forward Validation/1. Walk-forward validation as a more realistic choice.srt
3.0 kB
~Get Your Files Here !/12 - Prophet Modeling/1. Introduction to Prophet A semi-automatic time series model.srt
2.8 kB
~Get Your Files Here !/12 - Prophet Modeling/5. Configure seasonality parameters in Prophet.srt
2.8 kB
~Get Your Files Here !/5 - Model Time Series to Forecast Baseline Models/1. Intuition behind forecasting models.srt
2.7 kB
~Get Your Files Here !/12 - Prophet Modeling/3. Feed holidays data into the model.srt
2.5 kB
~Get Your Files Here !/6 - Autoregressive Integrated Moving Average (ARIMA)/3. How ARIMA changes with parameters P, D, and Q.srt
2.2 kB
~Get Your Files Here !/7 - Seasonal Autoregressive Integrated Moving Average (SARIMA)/1. Introducing seasonal order with SARIMA model.srt
2.1 kB
~Get Your Files Here !/2 - Visualize Time Series Data/3. Set Plotly as pandas backend for plotting.srt
2.0 kB
~Get Your Files Here !/12 - Prophet Modeling/6. How to interpret diagnostics with robust models.srt
2.0 kB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/1. Search and download Federal Reserve Economic Data.srt
2.0 kB
~Get Your Files Here !/11 - Exponential Smoothing Models/1. SARIMA vs. exponential smoothing.srt
1.9 kB
~Get Your Files Here !/16 - Conclusion/1. Next steps.srt
1.6 kB
~Get Your Files Here !/8 - Data Stationarity/1. How does stationarity look in a time series.srt
1.5 kB
~Get Your Files Here !/1 - Foundations Load and Preprocess Time Series Data Files/3. Datetime components on different columns.srt
1.5 kB
~Get Your Files Here !/0 - Introduction/1. Why learn practical Python for time series forecasting.srt
1.1 kB
Get Bonus Downloads Here.url
180 Bytes
~Get Your Files Here !/Bonus Resources.txt
70 Bytes
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