5 ms·
My very first move with timeseries data is to get to frequency space as fast as I can.
by hprotagonist 7y ago
My very first move with timeseries data is to get to frequency space as fast as I can.
- 1996 7y ago+1. FFT is under appreciated.
- new2628 7y agoWhat do you mean? It's one of the most used algorithm in the history of mankind.
- hprotagonist 7y agoand not a single DS/ML person seems to have ever heard of it.
- teej 7y agoBecause most folks in DS/ML are working on predicting likelihood to press a button. You don’t need Fourier transforms for that.
- hprotagonist 7y agoyou do, depending on your feature preprocessing.
- XuMiao 7y agoCNN is essentially a FT with learnable kernels.
- hprotagonist 7y agowell, it’s certainly usually convolution. Hardly anyone knows why or how that connects to the FT though.
- scottlocklin 7y agoMost DS/ML people are useless chimps. FWIIW I've used it all the time, along with wavelets, cepstrums, lifters and Hilbert transforms. For timeseries with nonlinearities and lots of data points, it's the way to go. 9/10 times I'd rather hire a EE with signal processing background than a statistician or data scientist for time series work.
- sgillen 7y agoUnder appreciated by people just now getting into time series regression, who may be really excited about throwing deep learning at the problem. Edit: literally saw this happen two weeks ago by a PhD in electrical engineering.
- 1996 7y agoSo much that! Also under appreciated: plots and OLS. Start plotting before firing up the GPUs, then compare to standard OLS - like in this article! Suggested reading if you don't know OLS: ML from scratch.
- dnautics 7y agoWell to be fair if you use a convolutional recurrent architecture, you're doing it without realizing it.
- objektif 7y agoCan you give an example pls?
- hprotagonist 7y ago“bandpass with a butterworth then plot the FFT and phase”
- wenc 7y agoIf you would indulge me, what sorts of insights would this get you? (genuinely curious) All I can think of is identification of frequency modes.
- windsignaling 7y agoIn the spirit of the article, how can frequency domain representation be used for prediction / forecasting? Any examples you could share?
- wenc 7y agoGenuinely curious -- how do you create predictive time-series models in the frequency domain? (background: control systems)
- hprotagonist 7y agospeech recognition immediately jumps to mind
- wenc 7y agoSpeech recognition is generally not considered a time series problem though.
- hprotagonist 7y agowhat about speech is nontemporal?
- wenc 7y agoTime series problems are indeed temporal, but not all temporal problems are time series problems. Time series deals with time-specific features of a discrete sequence like autocorrelation, trends, seasonality, etc. whereas frequency domain methods deal with, well, frequency. Support you’re are looking at sales patterns over a long period of time, which has certain patterns. FFTs are unlikely to tell you much that is useful or predict much whereas time series methods can reveal patterns where the t is the independent variable.