Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
I default to Prophet (formerly FBProphet) for my work [which is business-y timeseries data], curious what others are doing.
* Statistical models (ETS, (V)ARIMA(X), etc)
* ML models (sklearn models, LGBM, etc)
* Many recent deep learning models (N-BEATS, TFT, etc)
* Seamlessly works on multi-dimensional series
* Models can be trained on multiple series
* Several models support taking in external data (covariates), known either in the past only, or also in the future
* Many models offer rich support for probabilistic forecasts
* Model evaluation is easy: Darts has many metrics, offers backtest etc
* Deep learning scales to large datasets, using GPUs, TPUs, etc
* You can do reconciliation of forecasts at different hierarchical levels
* There's even now an explainability module for some of the models - showing you what matters for computing the forecasts
* (coming soon): an anomaly detection module :)
* (also, it even include FB Prophet if you really want to use it)
Warning: I'm probably biased because I'm Darts creator.
"What library do I use?" is the wrong question. "What model do I use?" is the right question. Libraries are just part of the process of answering that question.
That said, high quality implementations of interesting times series models seem hard to come by, so it's still a legitimate question to ask about libraries. but consider the goal of asking about libraries: you want to find high-quality implementations of useful models, not a magic black box that you can crank data through.
Also, there's a new version; s/2/3 :)
As an example, imagine you want to calculate only a single sample into the future. Say furthermore that you have six input timeseries sampled hourly, and you don't expect meaningful correlation beyond 48h old samples.
You create 6x48 input features, take the single target value that you want to predict as output, and feed this into your run of the mill gradient boosted tree.
The above gives you a less complex approach than reaching for bespoke time-series stuff; I've personally have had success doing something like this.
If your regressor does not support multiple outputs, you can always wrap it in sklearns MultiOutputRegressor (or optionally RegressorChain; check it out). This is useful if, in the above example, you are not looking to predict only the next sample, but maybe the next 12 samples.
https://scikit-learn.org/stable/modules/generated/sklearn.mu...
So you could roll your own using lots of different libraries and do the feature engineering, but for plain jane business time series, fbprophet usually works pretty decently with minimum effort. Because most of the predictable variation in business time series is are patterns in human behavior, which are mostly regulated by the weekly and yearly cycle, interrupted a few times per year by holidays. (And the 24h cycle if you forecast intra-day).
That said, fbprophet is somewhat tuned for a few years of regular data (ideally not interrupted by pandemics), sampled at the daily frequency. If your data is different (e.g. weekly or intra-day) it can start to break in unexpected ways, and it becomes worthwhile to dive into the model and customize or roll your own.
One of the "auto ARIMA" packages in R (Forecast or Fable) could also be a good choice.
However I think Prophet is a good next step from there. It's at least a sensible starting point for your own feature engineering.
I always had a gut feeling that there was some kind of unifying principle between NN and tree ensemble models. I wonder if the latter is kind of like a highly quantized, compressed version of the former, which explains why tree ensembles seem to work better than NNs on "lower resolution" data.
A similar pkg is PyTorch-forecasting but overall I prefer DARTS, mainly because of devs responsiveness and better docs
Data science has become so easy and accessible nowadays that basically anyone who can write code can fit and use models. That's a great thing in general, but it means that those of us who do this for a living really should hold ourselves to higher standards.
Even if you are thoroughly mediocre at your job (like me) and aren't smart enough to come up with something like Prophet on your own, you absolutely do need the ability to reason about the models that you use, and to evaluate their performance correctly.
Don't ask me what they do with all of these, I'm just the guy who make sure the forecast keeps being reproducible.
Maintaining repos of training/data, APIfying the pipeline, deploying an ML processing pipeline with CI/CD, etc?
- Prophet - seems to be the current 'standard' choice
- ARIMA - Classical choice
- Exponential Moving Average - dead simple to implement, works well for stuff that's a time series but not very seasonal
- Kalman/Statespace model - used by Splunk's predict[1] command (pretty sure I always used LLP5)
- 2-3 anomalies in a row
- high deviation from expected
- multiple models all detected anomaly
[1] :https://docs.splunk.com/Documentation/Splunk/9.0.1/SearchRef...
To be fair, it's pretty hard to create generic models that are can robustly handle any random time series.
> - high deviation from expected
> - multiple models all detected anomaly
This is basically what statistical process control charts do for you. If you haven't learned about it already, I can recommend looking it up!
I've long wanted to write an open article series for someone like you but never gotten around to it. There's so much information out there, but you sort of have to piece it together on your own, which is suboptimal.
Get some forecasts quick => FB Prophet. It's not as good as they'd have you believe, but it's fast and analysts can play with it to some extent.
Outlier detection => Hand-rolled C++ ETS framework.
Multilevel predictions and/or more complex tasks => That's where neural models start to have the edge, but at that point it's a costly project. I like simpler stuff to start, moving to the big guns if/when it's needed.
1. Time series based forecast based on revenue (the one OP is referring to). All the statistical time-series models come here. I primarily used H2O.ai for this.
2. Conversion based revenue forecast (input -> pipeline, output -> revenue). This proved to be quite tricky as there was a time lag between pipeline creation and revenue conversion
3. Delphi-method: Got the sales/pre-sales folks on-ground to predict a bottom-up number and used that as a forecast.
Finally, I combined them by applying weightages to the above approaches - based on how accurate they were on the test dataset.
IMHO, Like many of them have pointed out - the model/assumptions are more important than the library. The job of a data scientist is to make the prediction as reliable and explainable as possible.
Besides that, I also like statsmodels as the docs are pretty good.
https://tsfresh.readthedocs.io/en/latest/
https://www.sktime.org/en/v0.8.2/api_reference/auto_generate...
Person doing the implementation wasn't a rock star coder so we couldn't fix the performance and complexity issues in time; it was left it out of the releas. Maybe with more expertise, tsfresh can add value. The experience was pretty off-putting for me, all in all. Maybe others have different experiences?
It can make the implementation and maintainability of a codebase better (IMHO), without sacrificing predictive power.
/e: I'm a bit out of this game for 2-3 years now, but Python had nothing comparable. Prophet is suboptimal at best. Some things are implemented here or there, but it's all over the place. So I agree that R really shines here.
I would generally prefer R for this kind of stuff as the experts generally write the code, but Darts seems OK and is well-tested, at the very least (haven't had a chance to use it in anger yet).
This is in part why we built Darts. Now I think we can say the situation is quite different. Darts offers many things offered by the R forecast package, and then some (for instance the ability to train ML models on large datasets made of multiple potentially high-dimensional series).
Edit: I do question why you think this is "easiest". Do you have some really unusual/specific modeling needs?
When modeling time series, you will want a model that is sensitive both to short term and longer term movements. In other words, a Long Term Short Term Memory (LSTM).
Sepp Hochreiter invented this concept in his Master's thesis supervised by Jürgen Schmidhuber in Munich in the 1990s; today, it's the most-cited type of neural network.
Here are papers describing it: https://people.idsia.ch/~juergen/rnn.html
In Python, you can use TensorFlow's LSTMCell class: https://www.datacamp.com/tutorial/lstm-python-stock-market
Mind you, in practice I've found these DL approaches overkill for simple problems of the "trends + cyclics + noise" kind.