Author(s): VARUN MISHRA

Originally published on Towards AI.

(p,d,q): The Understated Framework Behind Serious Forecasting

Forecasting is often treated as a technological problem — throw data into a model, tweak a few knobs, and wait for predictions to appear. The reality is more nuanced. Forecasting is a structural problem. It’s about understanding how a system changes over time and what those changes reveal about the future.

This article delves into the nuances of forecasting, emphasizing that it goes beyond simple technological solutions. It introduces the ARIMA model and its essential parameters—p, d, and q—explaining how they collectively shape our understanding of time series data. The author discusses why these elements remain pivotal in the era of advanced neural forecasting models, underscoring the importance of interpretability, statistical grounding, and predictive stability. As the article unfolds, it explores the intricate interplay of these parameters and how they contribute to forming effective forecasting models, ultimately highlighting PDQ as a vital framework in navigating uncertainties and improving prediction accuracy.

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Published via Towards AI