R-Time-Series-Task-View-Supplement VS sktime

Compare R-Time-Series-Task-View-Supplement vs sktime and see what are their differences.

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R-Time-Series-Task-View-Supplement sktime
1 8
2 7,454
- 1.7%
8.2 9.8
2 days ago 2 days ago
Python
- BSD 3-clause "New" or "Revised" License
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.

R-Time-Series-Task-View-Supplement

Posts with mentions or reviews of R-Time-Series-Task-View-Supplement. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-08-03.
  • R packages for finance
    2 projects | /r/algotrading | 3 Aug 2021
    The R packages I have used most are quantmod and packages for GARCH such as rugarch and packages for vector autoregressions, listed in the Time Series task view , another task view for which I have created a supplement.

sktime

Posts with mentions or reviews of sktime. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-02-14.

What are some alternatives?

When comparing R-Time-Series-Task-View-Supplement and sktime you can also consider the following projects:

tslearn - The machine learning toolkit for time series analysis in Python

darts - A python library for user-friendly forecasting and anomaly detection on time series.

timemachines - Predict time-series with one line of code.

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

scikit-hts - Hierarchical Time Series Forecasting with a familiar API

scikit-learn - scikit-learn: machine learning in Python

sysidentpy - A Python Package For System Identification Using NARMAX Models

greykite - A flexible, intuitive and fast forecasting library

sktime-dl - DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow

InceptionTime - InceptionTime: Finding AlexNet for Time Series Classification