Comparing Four Time Series Forecasting Methods: Prophet, DeepAR, TFP-STS, and Adaptive AR

Author(s): Shenggang Li

Originally published on Towards AI.

A practical evaluation of models from Meta, Amazon, Google, and a new adaptive AR approach

Time series forecasting is everywhere — in business, finance, retail, and even public policy. The challenge is simple to describe but hard to solve: we want to predict the future based on the past, even when trends shift or patterns change unexpectedly.

This article compares four popular time series forecasting methods—Prophet, DeepAR, TFP-STS, and Adaptive Decay-Weighted AR—across various scenarios and datasets. Each model has unique strengths and weaknesses, particularly in handling different types of data and forecasting challenges. The analysis illustrates that while no single model emerges as the superior choice for every situation, understanding the nuances of each method allows practitioners to select the most appropriate tool for their specific forecasting needs, be it interpretability, computational efficiency, or accuracy.

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Published via Towards AI