Deseasonalized and Interpolated Quarterly Time Series Data

• Day: 2024-09-28
• Time: 20:30 to 20:55
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Time Series, Deseasonalization, Interpolation, Python, Data Analysis

Description

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.

Evidence

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• event_ids: []