darts VS flow-forecast

Don't use an LSTM. Get up to date with SoTA methods and read the papers in the field. LSTMs are not the way forward. Read the papers I suggested. It would be very useful to come to grips with both the Time Series Repository (https://github.com/thuml/Time-Series-Library) and Darts (https://github.com/unit8co/darts) as these are widely used for research and in industry.

As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts. Darts implements a wide range of models and is fairly easy to use.

The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).

That said, there's some interesting stuff out there that I've been experimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).

3. darts

Looking forward to checking this out! How does this compare with darts[1]?

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

Darts is an open-source Python library by Unit8 for easy handling, pre-processing, and forecasting of time series. It contains an array of models, from standard statistical models such as ARIMA to deep neural networks. https://unit8co.github.io/darts/

active support

The new Forecast menu, featuring the open-source Darts Time Series library, offers script-friendly functionality. It's also easy to use. Don't have any data to load yet? Enter through the Stocks or Crypto menus.

I would recommend Darts in Python [1]. It's easy to use (think fit()/predict()) and includes

* 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

* 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

* 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.

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

-Flow

How does it compare to Flow Forecast? Honestly people rarely mention FF but I've found it much better than pytorch_forecasting and the like.

PyTorch forecasting has a lot of bugs in it. You should try posting on issue on the actual repository though. Also, I've found Flow Forecast to be an all around much better deep learning for time series forecasting/classification framework.

If you want to use deep learning then Flow Forecast is the best. Many of the latest deep learning models and easy hyper-parameter sweeps.

I haven't personally tried it, but here's a Github Repo called LIME for Time. I'm not sure about the state of attention visualization for timeseries but this repo has several models using attention.

Hey everyone, just released a new Flow Forecast a deep learning for time series forecasting framework written in PyTorch. For this new version we added models, fixed several annoying bugs, and created better error messages. See the improved framework and tutorials Link

This is actually a good case for deep learning techniques that create a learned time series embedding id and/or graph convolutions. The advantage of these methods is the can [learn spatial temporal dependencies across several time series](Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation). There are some implementations that you can find of these models that you can find in the repository that deep learning for time series repo I maintain.

I mean a lot of deep learning models are more interpretable than you would think. There are a lot of methods to explain model predictions. In the deep learning for time series forecasting framework that I help maintain for example we automatically create SHAP plots to show relevant features. You can also visualize the attention mechanism directly.