Comprehensive Tutorial on Time Series Modelling and Forecasting

Everyone repeat after me: &quot;we need a baseline model&quot;.<p>You should always try some &quot;dumb&quot; models first. You&#x27;d be surprised how hard is to beat (of course depends on your KPIs) a historical average model with a more sophisticated method.

To my amateur eyes, normally the method for dealing with &#x27;time series&#x27; is really just finding ways to turn a non-stationary distribution into a stationary distribution, where you can then apply classic statistical methods on them. So you&#x27;re just finding ways to factor out the time component in the data so you can use the standard non-time sensitive regression models on the transformed data.<p>It seems like it&#x27;s very challenging to either have time as a first-class component in the model or somehow treat the data points as not independent. Indeed most models require independence so often it seems like we try to force the data to look that way by smoothing and transformations. You can assume this anytime an algorithm is asking you to provide &#x27;Stationarity&#x27;. It just seems like trying to look for the keys (prediction) where the streetlight is (model distributions with nice calculation properties).

For the interested, here is an overview into neural forecasting from the folks at Amazon research:<p>Neural forecasting: Introduction and literature overview<p><a href="https:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;2004.10240.pdf" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;pdf&#x2F;2004.10240.pdf</a>

Is there any other good resource on time series modeling and forecasting other than exponential smoothing and variants of ARIMA? Pretty much every tutotial on the web is on exponential smoothing and ARIMA or some lazy LSTM tutorials.

The readers interested in this article are probably able to give me good advice. I&#x27;ve been collecting stats daily on myself for the past year (weight, activity, calories consumed, sleep hours, etc) and I would love to be able to explore and extract interesting trends and relationships from the data.<p>Is there an easy tool where I can just drop in all the data and it presents me with some sort of dashboard? I would love it if the tool could identify and present interesting relationships (i.e. weight and calories consumed are strongly correlated)<p>Does anyone know if something like that exists? Or should I start rolling my own using python&#x2F;pandas?

Currently learning gluonTS, SEEMS GOOD

What would be some good graduate programs (I&#x27;m thinking Master&#x27;s level) in the US that specialize in time series modeling and forecasting? Any available online?

Your page has a bigger focus on Google ads than the subject matter itself.

coincidentally, i posted this not too long ago:<p><a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=23045207" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=23045207</a>

Last time using it is 1981. Still relevant today in ML era?