Retail Sales Forecasting: An End-to-End Time Series Analysis

This project presents a comprehensive time series analysis and forecasting of retail sales for a brick-and-mortar store. Using historical data from 2000 to 2015, the project explores trends, seasonality, and other time series components to predict future sales and support strategic decision-making.

Problem Statement

The retail industry thrives on accurate demand forecasting to manage inventory and maximize profits. This project aims to forecast sales for a physical retail store using historical sales data. The analysis applies statistical and machine learning models, focusing on ARIMA and SARIMAX, to achieve accurate predictions.

Objectives

1. Perform Exploratory Data Analysis (EDA) to understand sales trends and seasonality.
2. Build and compare ARIMA and SARIMAX models for forecasting.
3. Evaluate model performance using RMSE and AIC metrics.
4. Provide actionable insights for better sales planning.

Methodology

1. Data Preparation

• Preprocessed historical sales data.
• Split the data into training and testing sets.

2. Exploratory Data Analysis

• Analyzed time series components, including trends, seasonality, and residuals.
• Visualized sales data with line plots and decompositions.

3. Forecasting Models

ARIMA (AutoRegressive Integrated Moving Average)

• Built ARIMA models with parameter tuning based on ACF and PACF plots.
• Evaluated models using AIC to identify the best fit.

SARIMAX (Seasonal ARIMA with Exogenous Variables)

• Extended the ARIMA model to include seasonal components and external variables.
• Compared the performance of SARIMAX against simpler ARIMA models.

4. Model Evaluation

• Used Root Mean Squared Error (RMSE) to evaluate prediction accuracy.
• Compared models based on AIC values and residual diagnostic plots.

5. Forecasting

• Predicted sales for the test data and generated future forecasts with confidence intervals.

Key Results

• ARIMA Models: Provided reliable forecasts based on historical patterns.
• SARIMAX Models: Improved accuracy by incorporating seasonality and exogenous variables.
• Best models demonstrated low RMSE, indicating precise predictions.

Tools and Libraries

• Python: pandas, numpy, matplotlib, seaborn
• Forecasting Models: statsmodels (ARIMA, SARIMAX)
• Evaluation Metrics: Root Mean Squared Error (RMSE), Akaike Information Criteria (AIC)