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Kats: One stop shop for time series analysis in Python

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Re: Kats: One stop shop for time series analysis in Python

#11

Post by dataviz1000 »

benboughton1 wrote: post #9

What are suggested online courses to learn about multi variable time series forecasting? My skill level is - ok with university level Biometrics but that was 10+ years ago, and I am web/self-taught python for web apps and automating GIS tasks.

Good question. I've been working on this too iterating through Youtube and Medium tutorials and working through all the notebooks I can find. The best examples I've found use LSTM for deep learning and vector autoregression (VAR) for classical statistical forecasting.

This series might be useful to you. https://www.youtube.com/watch?v=ZoJ2OctrFLA&list=PLvcbYUQ5t0...

Re: Kats: One stop shop for time series analysis in Python

#12

Post by StreakyCobra »

acidbaseextract wrote: post #7

The time series feature (TSFeature) extraction module in Kats can produce 65 features with clear statistical definitions, which can be incorporated in most machine learning (ML) models... I'd be curious about the performance of these. A time series featurization library I've liked the look of but haven't used for real is catch22: https://github.com/chlubba/catch22 In particular I like catch22's methodology: catch22 i…

There is also "tsfresh" [1] in the same domain that does «Automatic extraction of 100s of features». It filters the most useful features according to the given task, I quote: «This filtering procedure evaluates the explaining power and importance of each characteristic for the regression or classification tasks at hand.»

[1]: https://github.com/blue-yonder/tsfresh

Re: Kats: One stop shop for time series analysis in Python

#13

Post by refactor_master »

What are some ways to deal with large volumes of variable-length timeseries for real-time predictions? The best solutions I've tried myself all hinge on windowed-feature extraction or LSTMs. It generally works, but starts to fall apart when you're squeezed for data.

It seems that almost everywhere you look, every example has just one timeseries that needs to be dealt with. However, since the methods are much more "statistical" in nature, they can actually make meaningful predictions on a single sample.

Re: Kats: One stop shop for time series analysis in Python

#14

Post by esquire_900 »

refactor_master wrote: post #13

What are some ways to deal with large volumes of variable-length timeseries for real-time predictions? The best solutions I've tried myself all hinge on windowed-feature extraction or LSTMs. It generally works, but starts to fall apart when you're squeezed for data. It seems that almost everywhere you look, every example has just one timeseries that needs to be dealt with. However, since the methods are much more "st…

I would say manual feature extraction? Your custom extraction could reduce the variable lengths to a uniform dimension (same number of features for every input), which can then be used by almost any algorithm.

These automatic extractions are very statistical in nature indeed, but for some datasets domain insights are more valuable and give more usable features (in my opinion). I found quite some datasets where manual features + gradient boosted trees give better results then automated statistical methods. Often combinations give better results :)

Re: Kats: One stop shop for time series analysis in Python

#15

Post by hrzn »

For those interested in time series library, we are developing Darts [1], which focuses on making it easy & straightforward to build and use forecasting models. Out of the box it contains traditional models (such as ARIMA) as well as recent deep learning ones (like N-Beats). It also allows to easily train models on multiple time series (potentially scaling to large datasets), as well as on multivariate series (i.e., series made of multiple dimensions). It will soon support probabilistic forecasts as well.

[1] https://github.com/unit8co/darts/

Re: Kats: One stop shop for time series analysis in Python

#16

Post by hrzn »

refactor_master wrote: post #13

What are some ways to deal with large volumes of variable-length timeseries for real-time predictions? The best solutions I've tried myself all hinge on windowed-feature extraction or LSTMs. It generally works, but starts to fall apart when you're squeezed for data. It seems that almost everywhere you look, every example has just one timeseries that needs to be dealt with. However, since the methods are much more "st…

For training forecasting models on multiple time series (and potentially large datasets), you can take a look at Darts [1] and the blog post [2].

[1]: https://github.com/unit8co/darts/

[2]: https://medium.com/unit8-machine-learning-publication/traini...

Re: Kats: One stop shop for time series analysis in Python

#18

Post by seertaak »

refactor_master wrote: post #13

What are some ways to deal with large volumes of variable-length timeseries for real-time predictions? The best solutions I've tried myself all hinge on windowed-feature extraction or LSTMs. It generally works, but starts to fall apart when you're squeezed for data. It seems that almost everywhere you look, every example has just one timeseries that needs to be dealt with. However, since the methods are much more "st…

Maybe lookup panel data and repeated experiments. Those techniques are applied when the data is "tabular"; there are often relatively few observations on any individual time axis, but there are many instances of these experiments. It's a branch of linear forecasting (least squares), but it's tailored for example for biological experiments where you have several sets of results - related but maybe not performed in the same lab - which you want to amalgamate.

Re: Kats: One stop shop for time series analysis in Python

#19

Post by streamofdigits »

hrzn wrote: post #15

For those interested in time series library, we are developing Darts [1], which focuses on making it easy & straightforward to build and use forecasting models. Out of the box it contains traditional models (such as ARIMA) as well as recent deep learning ones (like N-Beats). It also allows to easily train models on multiple time series (potentially scaling to large datasets), as well as on multivariate series (i.e.,…

is the focus on wrapping existing algorithms (like statsmodels) or are you developing at that level as well?
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