📅 2024-09-28 — Session: Enhanced Time Series Data Processing and Forecasting

🕒 17:55–19:35
🏷️ Labels: Python, Time Series, Data Processing, Forecasting, Pandas
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The goal of this session was to enhance the processing and forecasting capabilities for time series data using Python, focusing on trend and seasonality extrapolation, data merging, and error handling.

Key Activities

  • Developed a Python function for trend plus deviation extrapolation using linear regression and median deviations.
  • Created methods for calculating monthly residuals and forecasting with seasonal deviations.
  • Updated extrapolation functions to handle multiple columns in a pandas DataFrame.
  • Improved employment data processing by including all relevant variables and optimizing the code.
  • Streamlined EMAE data loading and processing, focusing on seasonality profiles.
  • Developed a function to merge DataFrames without duplicate columns, ensuring clean data output.
  • Enhanced DataFrame extrapolation and concatenation methods to avoid overlapping dates.
  • Addressed DataFrame column reference errors and optimized time series data handling using indice_tiempo.
  • Adapted functions to improve efficiency and error handling in time series forecasting.

Achievements

  • Successfully implemented and tested multiple Python functions for improved data processing and forecasting.
  • Enhanced the robustness and efficiency of time series data handling and analysis.

Pending Tasks

  • Further testing and validation of the updated functions with larger datasets.
  • Documentation and integration of the new methods into existing workflows.