Deseasonalized and Interpolated Quarterly Time Series Data

📅 2024-09-28 — Session: Deseasonalized and Interpolated Quarterly Time Series Data

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

Session Goal

The goal of this session was to refine quarterly time series data by removing seasonal biases and transforming the data into a monthly frequency using advanced techniques.

Key Activities

• Implemented deseasonalization of quarterly data using the seasonal_decompose function from the statsmodels library.
• Addressed duplicate time index issues and updated the frequency of the dataset to quarterly.
• Applied STL (Seasonal and Trend decomposition using LOESS) for enhanced seasonal decomposition.
• Utilized linear interpolation to transform quarterly data into a monthly frequency, ensuring smooth transitions between data points.
• Removed fixed biases from the dataset to improve accuracy and reliability of the time series analysis.

Achievements

• Successfully deseasonalized and interpolated the quarterly time series data, improving its granularity and accuracy for further analysis.
• Developed Python scripts for each method, ensuring reproducibility and ease of application for similar datasets.

Pending Tasks

• Further validation of the deseasonalized and interpolated data against actual observations to ensure consistency and accuracy.
• Exploration of additional decomposition techniques to enhance the robustness of the analysis.