Time-Series Forecasting: Deep Learning vs Statistics — Who Wins?
A comprehensive guide on the ultimate dilemma
In recent years, Deep Learning has made remarkable progress in the field of NLP.
Time series, also sequential in nature, raise the question: what happens if we bring the full power of pretrained transformers to time-series forecasting?
However, some papers, such as [2] and [3] have scrutinized Deep Learning models. These papers do not present the full picture. Even for NLP cases, some people attribute the breakthrough of GPT models to “more data and computing power” instead of “better ML research”.
This article aims to clear the confusion and provide an unbiased view, using reliable data and sources from both academia and industry. Specifically, we will cover:
- The pros and cons of Deep Learning and Statistical Models.
- When to use Statistical models and when Deep Learning.
- How to approach a forecasting case.
- How to save time and money by selecting the best model for your case and dataset.
Let’s dive in.
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