Time Series Forecasting of Seasonal Item Sales with Machine Learning: A Review

Abstract

Time series forecasting is pivotal in anticipating and comprehending seasonal products' sales patterns, facilitating organizations in optimizing inventory management, marketing campaigns, and overall resource utilization. Seasonal products with cyclical demand variations create special challenges in forecasting because their demand is subject to time-dependent variables like festivals, holidays, weather conditions, and other recurring phenomena. Precise forecasting of these sales trends helps organizations make better decisions, reduce costs, and enhance profitability.

Over the past few years, machine learning (ML) methods have demonstrated significant potential in enhancing seasonal sales forecast accuracy. This paper provides an in-depth review of these methods in comparison with the conventional ones to identify the breakthrough introduced by ML. Conventional models like Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing have been widely used for time series analysis owing to their interpretability and simplicity. But these approaches tend to fail to identify intricate nonlinear relationships and dynamic patterns in data, so they are not as good for long-term forecasting.

In contrast, machine learning methods provide the capability to discover complex patterns and relationships within sales data and increase forecasting accuracy. Sophisticated models like recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and ensemble learning techniques have been advanced as powerful analytics tools for forecasting seasonal sales trends. These models utilize temporal relationships and can deal with big data with more flexibility.

In addition, this paper discusses the significance of data preprocessing methods, including missing data handling, seasonal decomposition, and feature engineering, that are critical to improving model performance.

Metrics for evaluation are also discussed to determine the efficiency of different models in forecasting tasks.

Additionally, real-world usage of such forecasting models is addressed in industries ranging from retail and e-commerce to supply chain management, where precise forecasts facilitate optimization of inventory management, marketing strategies, and procurement planning. Lastly, the paper identifies the present-day problems of seasonal sales forecasting as being data quality issues, interpretability of the models, and integration of external influences, while offering some future directions of research for resolving such issues.

By presenting a holistic view of traditional and machine learning-based techniques, this review aims to provide insights into the strengths and limitations of each approach, ultimately guiding researchers and practitioners toward more accurate and reliable forecasting methods for seasonal item sales.