Predicting Sales in Retail Using Time Series Analysis

Introduction

Predicting sales in the retail sector is crucial for inventory management, resource allocation, and strategic planning. This project proposal outlines a system that leverages time series analysis to forecast future sales based on historical data. The aim is to enhance decision-making processes within retail businesses by providing accurate sales predictions.

Background

Time series analysis has become an essential tool in forecasting due to its ability to model temporal dependencies and patterns. Techniques such as Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks have shown significant promise in capturing trends and seasonality in sales data. The integration of these models can lead to improved accuracy in sales predictions.

Project Objective

The primary objective of this project is to develop a robust sales prediction system using a combination of ARIMA and LSTM models. This system aims to outperform traditional methods by incorporating advanced feature extraction techniques and leveraging large-scale retail datasets.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets such as the Walmart Store Sales dataset from Kaggle, which includes historical sales data across multiple stores.
• Feature Extraction: Extract relevant features such as promotional events, holidays, and economic indicators that influence sales patterns.

2. Model Architecture

• ARIMA Model: Implement ARIMA for capturing linear temporal dependencies and trends in the data.
• LSTM Model: Use LSTM networks to model complex patterns and non-linear relationships in the time series data.

3. Training and Evaluation

• Training: Use historical sales data to train the models, employing techniques like cross-validation for robust performance.
• Evaluation Metrics: Measure performance using metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

Expected Outcomes

The proposed system is expected to achieve higher accuracy in sales prediction compared to traditional statistical methods. By utilizing both ARIMA and LSTM models, the system should effectively handle variations in sales patterns across different stores and time periods.

Conclusion

This project aims to advance the field of retail sales forecasting by developing a state-of-the-art system capable of accurately predicting future sales. The integration of ARIMA and LSTM models is anticipated to provide significant improvements in forecast accuracy.

For further details on related research, please refer to the paper "Predicting Sales in Retail Using Time Series Analysis," available at https://ieeexplore.ieee.org/document/8489208.

The dataset used for this project can be accessed at Kaggle Store Sales - Time Series Forecasting.