Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

ArXiv ID: 2308.11939 “View on arXiv”

Authors: Unknown

Abstract

Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction models to gain a competitive edge. However, existing literature mostly focuses on historical sales data and ignores the vital influence of macroeconomic conditions on consumer spending behavior. In this study, we bridge this gap by enriching time series data of customer demand with macroeconomic variables, such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and unemployment rates. Leveraging this comprehensive dataset, we develop and compare various regression and machine learning models to predict retail demand accurately.

Keywords: Demand Forecasting, Time Series Analysis, Macroeconomic Variables, Machine Learning, Retail

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.2/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning models and statistical methods like LSTM and SARIMA, indicating moderate to high math complexity, and uses real-world macroeconomic datasets with comparative model evaluations, demonstrating strong empirical rigor.

  flowchart TD
    A["Research Goal: Accurate retail demand forecasting with macroeconomic variables"] --> B["Data Collection & Integration"]
    B --> C{"Data Inputs"}
    C --> C1["Historical Sales Data"]
    C --> C2["Macroeconomic Indicators<br>CPI, ICS, Unemployment"]
    B --> D["Model Development & Comparison"]
    D --> E["Computational Process"]
    E --> E1["Regression Models"]
    E --> E2["Machine Learning Models"]
    E --> E3["Forecasting Execution"]
    E --> E4["Performance Evaluation<br>MAE, RMSE"]
    E4 --> F["Key Findings"]
    F --> F1["Macroeconomic variables significantly improve forecast accuracy"]
    F --> F2["ML models outperform traditional regression"]
    F --> F3["Integrated approach essential for supply chain efficiency"]