#ARIMA

Articles about ARIMA — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with arima. ← All posts

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Pratik Dhanave · ·6 min read

Classical Methods — Exponential Smoothing and ARIMA

Before neural networks, two families of methods dominated forecasting for decades — and they still win surprisingly often. Exponential smoothing and ARIMA are the classical workhorses: well-understood, fast, interpretable, and shockingly hard to beat on many real series. This post explains both without heavy math, and makes the case that for a lot of forecasting, the right answer is still a classical model, not a deep one.

Before neural networks, two families dominated — and still win surprisingly often. Exponential smoothing (weighted memory of the past; Holt-Winters) and ARIMA (modeling autocorrelation directly). Why classical methods remain state-of-the-art for much real forecasting (strong on limited data, fast, interpretable, hard to beat — the M-competition result).

#Time Series#ARIMA#Exponential Smoothing#Forecasting#Machine Learning

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.