Enhanced Time Series Extrapolation and Data Processing

• Day: 2024-09-28
• Time: 17:55 to 19:35
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Python, Data Analysis, Time Series, Extrapolation, Pandas

Description

Session Goal

The session aimed to enhance time series data analysis by refining extrapolation functions and data processing methods.

Key Activities

• Implemented a Python function for trend plus deviation extrapolation using linear regression and median deviations.
• Developed methods to calculate monthly residuals and forecast using seasonal deviations, specifically for EMAE and employment data.
• Updated the trend_plus_seasonality_extrapolation function for handling multiple columns in a pandas DataFrame.
• Improved Python code for processing employment data, ensuring comprehensive inclusion of variables for analysis.
• Streamlined data loading and processing for EMAE, including seasonality profile calculations.
• Enhanced data merging functions to avoid duplicate columns and ensure clean data output.
• Optimized DataFrame extrapolation and concatenation methods to prevent overlapping dates.
• Addressed DataFrame column reference errors and optimized time series data handling using indice_tiempo.

Achievements

• Successfully updated and integrated extrapolation functions for more efficient time series forecasting.
• Improved data processing scripts for both employment and EMAE datasets, enhancing data analysis capabilities.

Pending Tasks

• Further testing and validation of the updated functions on additional datasets to ensure robustness.
• Exploration of additional optimization techniques for large-scale data handling.

Evidence

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• event_ids: []