📅 2023-10-24 — Session: Enhanced Time Series Regression and Data Visualization
🕒 03:00–05:20
🏷️ Labels: Python, Time Series, Linear Regression, Data Visualization, Pandas
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal: The session aimed to enhance the robustness of time series regression analysis and improve data visualization techniques using Python.
Key Activities:
- Developed methods for detecting exponential trends in time series data, specifically for the year 2021, by applying data filtering, transformation, and linear regression.
- Addressed code execution issues that led to data loss, ensuring data integrity for continued analysis.
- Implemented checks for -∞ values and NaN handling in logarithmic transformations to ensure robust data handling during linear regression.
- Improved linear regression code for better handling of NaN and infinite values, enhancing code readability and robustness.
- Resampled time series data to a monthly frequency to ensure comparability across different series.
- Converted exponential detection results into a DataFrame using Pandas for easier analysis.
- Merged DataFrames with MultiIndex to align time series data properly.
- Enhanced plot aesthetics using Matplotlib and Seaborn, focusing on gridlines, legends, and overall visual appeal.
Achievements:
- Developed a robust Python script for time series regression, capable of handling NaN and infinite values effectively.
- Successfully merged and manipulated DataFrames with MultiIndex, facilitating complex data operations.
- Improved plot aesthetics and visualization techniques, resulting in clearer and more informative data presentations.
Pending Tasks:
- Further testing and validation of the enhanced regression code on additional datasets to ensure its generalizability.
- Exploration of additional visualization techniques to further enhance data presentation.