Most of Hyndman's textbook approaches (mostly ARIMA and various exponential smoothers) are implemented in his 'forecast' R package.
ARIMA and exponential smoothers tend to be a bit hard to get working well on daily data (they come from the era where most data was monthly or quarterly). A modern take on classical frequency domain Fourier regression is Facebook Prophet (https://facebook.github.io/prophet/) which tends to work pretty well if you have a few years of daily data( https://facebook.github.io/prophet/ )
Anyone know of good resources for multivariate, multimodal, irregular timeseries forecasting? I know some great practical tools and tutorials (prophet, fast.ai), but I'd love to inject some statistical knowledge like FPP offers.
Reading his book at the very least will give a lot of insights to the standard of practice for people writing forecasting in the R world.
There are multivariate models but I don't know much about those. Most of the good resources are in the econometric domain. Multivariate time series within econometric, from what I've seen, is portfolio balancing.
For a general overview for statistic domain I would recommend:
For ARIMA I love this book:
Time Series Econometric by Levendis
For GARCH: Financial Modeling Under Non-Gaussian Distributions
If you want to learn more within statistic and time series in medical data: there is (1) longitudinal and (2) survival analysis. There are non linear time series but those are rare because most of our tools work within linear. There are also circular time series and temporal spatial statistic but I don't have any relevant knowledge in those to give you. I'm sure there are other that I don't know about within statistic.
Another interesting one is change point statistic https://en.wikipedia.org/wiki/Change_detection.
There is also a coursera course in time series that I've taken. I will post it here when I get off of work and better internet connection.
If you want an idea what forecast models out there you should read the papers from https://en.wikipedia.org/wiki/Makridakis_Competitions
There are 4 papers now and most of them are on statistical models which traditional dominating this domain. Datascience/ML models are slowing getting in there. M4 the best model was a highly tailor hybrid between ML/Stat technique the person who created it was employed by Uber and wrote an article about it.
The 5th competition m5 is currently underway and split into 2 contest. I'm eagerly waiting to read the paper on the results.
[0] https://www.amazon.com/Introduction-High-Frequency-Finance-R...
You should always try some "dumb" models first. You'd be surprised how hard is to beat (of course depends on your KPIs) a historical average model with a more sophisticated method.
If baseline beats NN, present NN as the baseline, and say you have an algorithm even better than NN.
(Joke only.)
“By inducing global knowledge indirectly from co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren.” (http://www.stat.cmu.edu/~cshalizi/350/2008/readings/Landauer...)
It seems like it'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 'Stationarity'. It just seems like trying to look for the keys (prediction) where the streetlight is (model distributions with nice calculation properties).
I'm interested in methods that don't "subtract" simple "trends" and "seasonality" from the data (which may work for bog-standard templates such as sales data but not what I'm interested in), and rather responds to sequential relationships in the data itself, that exploits exactly the correlations you describe directly.
Actually, time is a valuable feature. Eg, if stock goes sideways too long day traders will get out of the trade even if it didn't go up to the levels they were looking for. Also, eg, if the market goes up a lot beyond a trader's expectations in a short amount of time, often time a trader will wait a little bit longer. Likewise, many of the popular indicators day traders use today to be profitable have time as a key ingredient, eg TD.
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)
Does anyone know if something like that exists? Or should I start rolling my own using python/pandas?
[1] https://online.stat.psu.edu/statprogram/ [2] https://online.stat.psu.edu/statprogram/stat510
Neural forecasting: Introduction and literature overview
