telegram.bot
forecast
| telegram.bot | forecast | |
|---|---|---|
| 1 | 2 | |
| 106 | 1,099 | |
| - | - | |
| 0.0 | 7.1 | |
| 7 months ago | 17 days ago | |
| R | R | |
| GNU General Public License v3.0 only | - |
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telegram.bot
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Get Whatsapp notification when job ends
Sure. Try checking out github repo of the package I recommend first: https://github.com/ebeneditos/telegram.bot
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
What are some alternatives?
hts - Hierarchical and Grouped Time Series
parsel - parallel execution of RSelenium
R.matlab - R package: R.matlab
lmForc - R package for evaluating linear forecasting models.
future - :rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone
Peptides - An R package to calculate indices and theoretical physicochemical properties of peptides and protein sequences.
meta - Official Git repository of R package meta
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.