2dayiownu t1_j8wpinb wrote on February 17, 2023 at 2:25 PM

Temporal Fusion Transformer

emotionalfool123 t1_j8wvvda wrote on February 17, 2023 at 3:09 PM

This lib has lot of implementations including one you mentioned.

https://unit8co.github.io/darts/index.html

dj_ski_mask t1_j8x2m11 wrote on February 17, 2023 at 3:54 PM

I am knee deep in this library at work right now.

Pros: they implement tons of algos and regularly update with the ‘latest and greatest,’ like NHITS. Also can scale with GPUs/TPUs for the algos that use Torch backend. Depending on the algo you can add covariates and the “global” models for multivariate time series are impressive in their performance.

Cons: my god it’s a finicky library that takes considerable time to pick up. Weird syntax/restrictions for scoring and evaluating. Differentiating between “past” and “future” covariates is not as cut and dried as documentation makes it seem. Also, limited tutorials and examples.

All in all I like it and am making a speed run to learning this library for my time series needs.

To OP I would suggest NHITS, but also, the tree based methods STILL tend to win with the data I work with.

emotionalfool123 t1_j8x49h8 wrote on February 17, 2023 at 4:04 PM

Then it seems this is equivalent to the confusion R timeseries libraries cause.

clisztian t1_j8z3t1r wrote on February 17, 2023 at 11:59 PM

I guarantee you a state space model will beat out any fancy named transformer for most “forecastable” problems. Even MDFA - signal extraction + exp integration for forecasting - will beat out these big ML models

dj_ski_mask t1_j9345mc wrote on February 18, 2023 at 9:44 PM

I should mention with some tuning I have been able to get NHITS to outperform Naive Seasonal, CatBoost with lags, and ES models, so it’s not terrible.