Intermittent Demand Forecasting: Unraveling Sporadic Patterns with Precision

Welcome to the "Intermittent Demand Forecasting" project, where we delve deep into the intricacies of time series data characterized by sporadic demand patterns. This unique data phenomenon, often encountered in inventory management and supply chain optimization, presents challenges due to frequent periods of zero demand interspersed with sporadic bursts of activity.

Understanding Intermittent Demand Intermittent demand refers to a time series pattern where demand occurs irregularly, with many periods exhibiting zero demand. This poses a significant challenge for traditional forecasting techniques, necessitating specialized approaches to accurately predict future demand fluctuations.

Tech Stack

• Python: The project is built entirely in Python, leveraging its rich ecosystem of libraries and tools for data analysis, modeling, and visualization.
• skforecast Library: We utilize the skforecast library, specifically designed for time series forecasting tasks, to develop and evaluate forecasting models tailored to intermittent demand data.
• Pandas and NumPy: These foundational libraries are used extensively for data manipulation, preprocessing, and exploratory data analysis.
• Matplotlib and Seaborn: For data visualization, we rely on Matplotlib and Seaborn to create informative plots and charts that aid in understanding data patterns and model performance.

Key Components of the Project

• Data Exploration and Understanding: We begin by conducting thorough exploratory analysis to gain insights into the intermittent demand data's characteristics, including zero-demand periods, sporadic bursts, and seasonality.
• Lag Identification: Identifying optimal lag configurations is crucial for capturing temporal dependencies in intermittent demand data. We perform lag analysis and visualize autocorrelation plots to determine the most effective lag lengths for forecasting.
• Backtesting: Rigorous backtesting methodologies are employed to evaluate the forecasting models' performance. Backtesting allows us to simulate forecasting on historical data and assess the models' accuracy and reliability.
• Hyperparameter Tuning with Grid Search: Precision is paramount! We utilize grid search techniques to systematically explore hyperparameter combinations and optimize model performance, ensuring the best possible forecasting accuracy.