Vol. 8, Special Issue 2 (2019)

Traditional time series models, including autoregressive integrated moving average (ARIMA), exponential smoothing methods, and autoregressive integrated moving average with exogenous variables (ARIMAX), have long been the cornerstone of sales prediction. Their simplicity and interpretability have made them the preferred choice in various industries. However, these models often struggle to capture the complex patterns inherent in sales data, especially when confronted with non-linear relationships and high-dimensional input spaces.

In contrast, deep learning approaches, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), have emerged as formidable contenders in sales prediction. Their ability to automatically learn intricate features from vast and unstructured data sets allows them to model the dynamic nature of sales patterns more effectively. Deep learning models excel in capturing temporal dependencies, seasonality, and non-linear trends, offering a promising alternative to traditional methods.

Despite their undeniable success, deep learning approaches are not without their limitations. The insatiable appetite for large volumes of labeled data and computational resources poses a considerable challenge, especially for organizations with limited datasets and computing capabilities. Additionally, the 'black-box' nature of deep learning models raises interpretability concerns, hindering their widespread adoption in industries where transparency is paramount.

This paper critically evaluates the strengths and weaknesses of both traditional time series models and deep learning approaches, offering insights into their applicability across diverse scenarios. The review emphasizes the importance of a nuanced approach, advocating for a hybrid methodology that leverages the interpretability of traditional models and the predictive power of deep learning techniques. Through a comprehensive analysis, this review aims to guide practitioners and researchers in making informed decisions when selecting the most suitable methodology for sales prediction in their specific contexts.