Product Forecasting through Time Series Analysis (Modelling)

2026-02-13 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, long

Summary

This article details a structured approach to product sales forecasting using time series analysis, focusing on designing and evaluating models with machine learning, deep learning, and statistical techniques. The process begins with region-level data segregation to capture unique sales behaviors, followed by data preprocessing, feature engineering, and model selection. Models evaluated include Linear Regression, XGBoost, SARIMAX, and Facebook Prophet, trained on region-level sales data from January 1, 2018, to May 31, 2019. Performance was assessed using MAPE, MSE, MAE, and R² scores. The framework extends to store-level predictions, identifying top-performing stores and visualizing future sales trends. Linear Regression and XGBoost demonstrated high accuracy, while LSTM models struggled due to limited data. SARIMAX and Prophet showed moderate performance, with Prophet outperforming SARIMAX.

Key takeaway

For Data Scientists building product sales forecasting systems, prioritize a hierarchical modeling strategy, starting with region-level analysis before drilling down to stores. Your initial model selection should favor feature-based machine learning models like Linear Regression and XGBoost, as they often outperform deep learning models like LSTMs on smaller time-series datasets. Ensure you incorporate lag features and rolling means, and consider exogenous variables like discounts to capture critical sales drivers, leading to more accurate and actionable store-level insights.

Key insights

Effective product sales forecasting requires a hierarchical, multi-model approach combining ML, deep learning, and statistical methods.

Principles

• Region-level segregation captures unique sales patterns.
• Lag features are critical for ML time-series models.
• Discounts positively impact sales.

Method

The method involves region-level data segregation, feature engineering (one-hot encoding, date conversion, Min-Max scaling, lag features, rolling mean), training diverse models (Linear Regression, XGBoost, SARIMAX, Prophet), and evaluating performance with MAPE, MAE, MSE, and R².

In practice

• Use Min-Max scaling to prevent data leakage.
• Incorporate 7-day lag features for weekly seasonality.
• Use recursive forecasting for realistic store-level predictions.

Topics

• Product Sales Forecasting
• Time Series Analysis
• Machine Learning Models
• Statistical Forecasting
• Feature Engineering

Code references

• deeptinikumbh/Product-Sales-Forecasting-

Best for: Machine Learning Engineer, Data Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.