Time Series Forecasting vs Regression: An informal guide

The author makes a call out to the online book Forecasting: Principles and Practice which is a great reference when conducting time series analyses. <a href="https:&#x2F;&#x2F;otexts.com&#x2F;fpp3&#x2F;" rel="nofollow">https:&#x2F;&#x2F;otexts.com&#x2F;fpp3&#x2F;</a>

ARIMA models, seasonal adjustments,... this is still largely based on the Box-Jenkins method (developed in the 70&#x27;s!). I feel like this stuff has been taught the same way for decades now (maybe similar to undergraduate classical mechanics or other topics that are considered &#x27;solved&#x27;). Is this really still the state of the art? Time series analysis seems oddly close to machine learning, which seems to move at break-neck speed all the time, yet it feels completely stuck in time. Can someone unravel that paradox for me?

I always feel this is too close to stochastic versus random. There is a lot of text that pushes an idea that regression is used to understand how well a model fits relationships between variables. But, I start to have major doubts when people push the idea that regression models are not also predictive models.

To me time is just one dimension. What is described is just the difference between interpolation and extrapolation.<p>In terms of forecasting state of the art are weather models like graphcast or panguweather. I guess arima won&#x27;t be much of help in those high dimensional cases.<p>If you consider the univariate case the trick to outperform arima I guess is to detect the context from the time window before to make better contextual predictions: this is much like a regression on a hidden variable.

With risk of sounding bad: I can ask chatgpt to summarize this for me without any human writing an article since there is ample knowledge already available. What is the future of these kind of articles ?

&gt; This process is typically called “feature engineering”, and is part art and part science. Choices on including or excluding certain variables, and how they are translated into numerical parameters, can significantly impact the model’s performance.<p>According to this article, to make good predictive&#x2F;regression model, we need a good artist and a good engineer!