forecast
future
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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
future
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Running Code in Parallel
Check out the future package: https://github.com/HenrikBengtsson/future
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What's faster: run simultaneously on multiple terminals, or run everything sequentially on one terminal?
If you're a fan of the tidyverse check out the furrr package, which is based on the future package. Let's you apply your map() functions in parallel very easily.
What are some alternatives?
parsel - parallel execution of RSelenium
seurat - R toolkit for single cell genomics
lmForc - R package for evaluating linear forecasting models.
R-sharp - R# language is a kind of R liked vectorized language implements on .NET environment for the bioinformatics data analysis
Peptides - An R package to calculate indices and theoretical physicochemical properties of peptides and protein sequences.
HoRM - Supplemental Functions and Datasets for "Handbook of Regression Methods"
modeltime.ensemble - Time Series Ensemble Forecasting
hts - Hierarchical and Grouped Time Series
rtypeform - An R interface to the 'typeform' API.
r2u - CRAN as Ubuntu Binaries
openxlsx - openxlsx - a fast way to read and write complex xslx files