forecast
tsfel
| forecast | tsfel | |
|---|---|---|
| 2 | 1 | |
| 1,099 | 860 | |
| - | 2.9% | |
| 7.1 | 7.7 | |
| 20 days ago | 4 days ago | |
| R | Python | |
| - | BSD 3-clause "New" or "Revised" License |
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forecast
- Repost - R Package for Creating Linear Forecasting Models
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Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
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
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Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
For feature extraction check out tsfel:
https://github.com/fraunhoferportugal/tsfel
What are some alternatives?
parsel - parallel execution of RSelenium
tsfresh - Automatic extraction of relevant features from time series:
lmForc - R package for evaluating linear forecasting models.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
Peptides - An R package to calculate indices and theoretical physicochemical properties of peptides and protein sequences.
tsflex - Flexible time series feature extraction & processing
modeltime.ensemble - Time Series Ensemble Forecasting
upgini - Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs
rtypeform - An R interface to the 'typeform' API.
HoRM - Supplemental Functions and Datasets for "Handbook of Regression Methods"
future - :rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone