forecast
lmForc
| forecast | lmForc | |
|---|---|---|
| 2 | 4 | |
| 1,099 | 4 | |
| - | - | |
| 7.1 | 8.1 | |
| 19 days ago | about 2 years ago | |
| R | R | |
| - | GNU General Public License v3.0 only |
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forecast
- Repost - R Package for Creating Linear Forecasting Models
-
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
lmForc
What are some alternatives?
parsel - parallel execution of RSelenium
lmForc - R package for evaluating linear forecasting models.
Peptides - An R package to calculate indices and theoretical physicochemical properties of peptides and protein sequences.
modeltime.ensemble - Time Series Ensemble Forecasting
rtypeform - An R interface to the 'typeform' API.
HoRM - Supplemental Functions and Datasets for "Handbook of Regression Methods"
tsfel - An intuitive library to extract features from time series.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
future - :rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone
timetk - Time series analysis in the `tidyverse`
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.