Auto_TS
modeltime
| Auto_TS | modeltime | |
|---|---|---|
| 6 | 5 | |
| 674 | 501 | |
| - | 1.0% | |
| 6.8 | 8.4 | |
| 1 day ago | 4 months ago | |
| Jupyter Notebook | R | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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Auto_TS
modeltime
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Cross Validating Time Series Models in R
Check out the ModelTime package: https://business-science.github.io/modeltime/
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Good package or tidy way of sliding time series forecasting windows for backtesting?
I was looking for something similar a bit ago and settled on timetk and modeltime. It's been a while since I worked with these and I never got deep enough in my own project to fully explore them, so unfortunately all I can offer are the links; however this should get you what you're looking for
- Has anyone taken Matt Dancho's courses?
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Time Series in R
It’s actually completely different than what existed. You can see my roadmap here and how much has went into the Modeltime project. https://github.com/business-science/modeltime/issues/5
What are some alternatives?
Deep_XF - Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
fable - Tidy time series forecasting
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
ChatLog - ⏳ ChatLog: Recording and Analysing ChatGPT Across Time
modeltime.gluonts - GluonTS Deep Learning with Modeltime
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
boostime - The Tidymodels Extension for Time Series Boosting Models
logbrain - Parsing log files can be a tedious task, especially when dealing with complex log formats. The Log Parser aims to streamline this process by leveraging regular expressions to match and capture relevant fields from log entries. With the extracted data, users can perform further analysis, generate reports, or gain insights from their log files.
healthyR.ts - A time-series companion package to healthyR
TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python
modeltime.resample - Resampling Tools for Time Series Forecasting with Modeltime