Time Series Data Deseasonalization and Bias Removal

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📅 2024-09-28 — Session: Time Series Data Deseasonalization and Bias Removal

🕒 20:30–20:55
🏷️ Labels: Time Series, Deseasonalization, Bias Removal, Python, Data Analysis
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to deseasonalize quarterly time series data, handle duplicate time index issues, and remove fixed bias using Python and the statsmodels library.

Key Activities

  • Deseasonalizing Quarterly Data: Utilized the seasonal_decompose function to remove seasonality from quarterly data.
  • Handling Duplicate Time Index: Resolved issues with duplicate labels in the time index and updated the frequency to quarterly.
  • STL Decomposition: Implemented STL (Seasonal and Trend decomposition using LOESS) to better handle seasonality.
  • Linear Interpolation and Bias Removal: Transformed quarterly data into monthly frequency using linear interpolation and removed bias.
  • Seasonal Adjustment: Removed fixed seasonal bias and adjusted data to compute fixed residuals.

Achievements

  • Successfully implemented deseasonalization and bias removal techniques on quarterly data.
  • Managed to resample and visualize data effectively using Python.

Pending Tasks

  • Further analysis may be needed on the impact of bias removal on forecast accuracy.
  • Consider automating the process for larger datasets.

Graph View

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