Product Sales Forecasting through Time Series Analysis (EDA)

2026-02-12 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

This article details an exploratory data analysis (EDA) for product sales forecasting, focusing on univariate and bivariate analyses, time series decomposition, and data quality treatment. The analysis reveals that average sales distributions across four regions (R1-R4) are skewed, with Region R1 consistently showing the highest average sales and order volumes. Bivariate analysis highlights that Location Type L1, especially with Store Type S4, drives the highest sales across all regions. Discounted days significantly boost sales, while holidays tend to show lower sales. Time series decomposition indicates that sales are primarily seasonality-driven rather than trend-driven, exhibiting strong, consistent seasonal patterns across all regions. Outliers in sales and order quantities were identified using box plots and treated with the Interquartile Range (IQR) method, and categorical features were encoded for modeling.

Key takeaway

For data scientists and machine learning engineers building sales forecasting models, recognize that sales are predominantly seasonality-driven, not trend-driven. Your models, such as SARIMAX, Prophet, or LSTM, must explicitly capture these strong seasonal patterns. Align inventory planning and promotional strategies with recurring seasonal cycles, and prioritize data quality treatments like IQR-based outlier handling and careful categorical feature encoding to ensure robust model performance.

Key insights

Product sales are heavily influenced by seasonality, location, store type, and promotional activities, rather than long-term trends.

Principles

• EDA prepares data for predictive modeling by explaining "why" business events occur.
• Sales variability is influenced by both order count and average order value.
• Forecasting models must explicitly account for seasonal effects.

Method

The EDA process involves univariate and bivariate analysis, time series decomposition, outlier detection (IQR method), and categorical feature encoding to prepare data for robust sales forecasting.

In practice

• Use histograms to examine central tendency, spread, and skewness.
• Employ heatmaps to visualize relationships between categorical variables and sales.
• Cap outliers using the IQR method to preserve data volume.

Topics

• Product Sales Forecasting
• Exploratory Data Analysis
• Time Series Analysis
• Outlier Detection
• Feature Engineering

Code references

• deeptinikumbh/Product-Sales-Forecasting-

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

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