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- I elaborate on how to interpret and use time-series decomposition to analyze data. It covers methods to extract trend, seasonal, and residual components, providing practical guidance on applying these techniques.
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Time-Series Forecasting Versus Traditional Manufacturing
- I provide a detailed analysis on time-series forecasting techniques versus traditional manufacturing forecasting methods.
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Hierarchical Forecasting in Python
- I demonstrate how to apply hierarchical forecasting methods to aggregated datasets. In my project, I explore techniques for forecasting at different levels of data aggregation, showcasing practical implementation in Python.
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Long Short-Term Memory (LSTM) Forecasting in Python
- I showcase a LSTM model I used for time-series forecasting tasks in Python, leveraging deep learning for improved accuracy.
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Support Vector Regression (SVR) Forecasting in Python
- In this project, I elaborate on the application of Support Vector Regression (SVR) for time-series forecasting. I emphasize SVR's capability to handle non-linear relationships in data, demonstrating its use in predictive modeling.