Making forecasting self-learning and adaptive – Pilot forecasting rack

ArXiv ID: 2306.07305 “View on arXiv”

Authors: Unknown

Abstract

Retail sales and price projections are typically based on time series forecasting. For some product categories, the accuracy of demand forecasts achieved is low, negatively impacting inventory, transport, and replenishment planning. This paper presents our findings based on a proactive pilot exercise to explore ways to help retailers to improve forecast accuracy for such product categories. We evaluated opportunities for algorithmic interventions to improve forecast accuracy based on a sample product category, Knitwear. The Knitwear product category has a current demand forecast accuracy from non-AI models in the range of 60%. We explored how to improve the forecast accuracy using a rack approach. To generate forecasts, our decision model dynamically selects the best algorithm from an algorithm rack based on performance for a given state and context. Outcomes from our AI/ML forecasting model built using advanced feature engineering show an increase in the accuracy of demand forecast for Knitwear product category by 20%, taking the overall accuracy to 80%. Because our rack comprises algorithms that cater to a range of customer data sets, the forecasting model can be easily tailored for specific customer contexts.

Keywords: Time Series Forecasting, Demand Forecasting, Feature Engineering, Algorithm Selection, Retail Analytics, Retail / Consumer Goods

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders
• Why: The paper relies on standard ML techniques (XGBoost, LSTM) with minimal advanced math, but demonstrates strong empirical implementation using a real retail dataset, feature engineering, and clear accuracy metrics (RMSE, R^2).

  flowchart TD
    A["Research Goal: Improve demand forecast accuracy for low-accuracy categories like Knitwear (60%)"] --> B["Methodology: Algorithmic Rack Approach"]
    B --> C["Data Input: Historical sales & price data for Knitwear"]
    C --> D["Computational Process: Dynamic algorithm selection based on state/context"]
    D --> E["Advanced Feature Engineering & AI/ML Model Training"]
    E --> F["Outcomes: Forecast accuracy improved by 20% to 80%"]
    F --> G["Benefit: Model is adaptable to diverse customer datasets"]