parsel
parallel execution of RSelenium (by till-tietz)
forecast
Forecasting Functions for Time Series and Linear Models (by robjhyndman)
| parsel | forecast | |
|---|---|---|
| 1 | 2 | |
| 15 | 1,099 | |
| - | - | |
| 4.4 | 7.1 | |
| 5 months ago | 26 days ago | |
| R | R | |
| GNU General Public License v3.0 or later | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
parsel
Posts with mentions or reviews of parsel.
We have used some of these posts to build our list of alternatives
and similar projects.
forecast
Posts with mentions or reviews of forecast.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-30.
- 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
What are some alternatives?
When comparing parsel and forecast you can also consider the following projects:
nflfastR - A Set of Functions to Efficiently Scrape NFL Play by Play Data
lmForc - R package for evaluating linear forecasting models.