SARIMAX Time Series Forecasting
Advanced time series analysis using SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous factors)
10 min read
Overview
This notebook demonstrates the implementation of SARIMAX modeling for time series forecasting that I used in previous work experience at Mecca Brands. I have used Rossman Sales Data to showcase the application of this model. The model was used to improve sales prediction accuracy across all sales channels by over 90% and provide insights into incremental revenue generated through marketing campaigns.
Key Features
- Exploratory Data Analysis
- Seasonal decomposition of time series data
- Parameter optimization using grid search
- Integration with external factors (promotional events, holidays)
- Model validation and accuracy metrics
Technical Implementation
The model was implemented using auto_arima by PMDARIMA. It incorporates multiple seasonal patterns and external regressors to capture complex relationships in the data.
Business Impact
- 90%+ improvement in sales prediction accuracy
- Provided insights into revenue generated through promotional activities
- Used for financial planning and white paper sessions