Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • darts

    A python library for user-friendly forecasting and anomaly detection on time series.

    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

  • Prophet

    Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  • forecast

    Forecasting Functions for Time Series and Linear Models

    As a few other people have mentioned, I find R to be the easiest tool for this job, specifically the forecast package [0]. I had to use this package for an applied econometrics course in college a few years ago, and I have been using it ever since. I find the syntax to be more straightforward than comparable libraries in Python. I also assume that this library (and other libraries in R) offer higher quality models and results than their counterparts in Python, but this is just an assumption.

    [0] https://github.com/robjhyndman/forecast

  • tsfel

    An intuitive library to extract features from time series.

    For feature extraction check out tsfel:

    https://github.com/fraunhoferportugal/tsfel

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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