modeltime.ensemble | Time Series Ensemble Forecasting | Time Series Database library

by business-science R Version: V0.4.2 License: Non-SPDX

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | modeltime.ensemble Summary

modeltime.ensemble is a R library typically used in Database, Time Series Database applications. modeltime.ensemble has no bugs, it has no vulnerabilities and it has low support. However modeltime.ensemble has a Non-SPDX License. You can download it from GitHub.

Ensemble Algorithms for Time Series Forecasting with Modeltime. A modeltime extension that implements ensemble forecasting methods including model averaging, weighted averaging, and stacking.

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modeltime.ensemble has a low active ecosystem.

It has 67 star(s) with 14 fork(s). There are 6 watchers for this library.

It had no major release in the last 12 months.

There are 12 open issues and 8 have been closed. On average issues are closed in 4 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of modeltime.ensemble is V0.4.2

Quality

modeltime.ensemble has 0 bugs and 0 code smells.

Security

modeltime.ensemble has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

modeltime.ensemble code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

modeltime.ensemble has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

modeltime.ensemble releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 5675 lines of code, 0 functions and 45 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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modeltime.ensemble Key Features

No Key Features are available at this moment for modeltime.ensemble.

modeltime.ensemble Examples and Code Snippets

modeltime.ensemble ,Make Your First Ensemble in Minutes

Lines of Code : 40

License : Non-SPDX (NOASSERTION)

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library(tidymodels)
library(modeltime)
library(modeltime.ensemble)
library(tidyverse)
library(timetk)

m750_models
#> # Modeltime Table
#> # A tibble: 3 x 3
#>   .model_id .model     .model_desc            
#>

modeltime.ensemble ,Installation

Lines of Code : 2

License : Non-SPDX (NOASSERTION)

Copy

install.packages("modeltime.ensemble")

remotes::install_github("business-science/modeltime.ensemble")

Community Discussions

Trending Discussions on modeltime.ensemble

How to extract confidence intervals from modeltime recursive ensembles?

QUESTION

How to extract confidence intervals from modeltime recursive ensembles?

Asked 2021-Dec-02 at 16:17

As I want to produce some visualizations and analysis on forecasted data outside the modeltime framework, I need to extract confidence values, fitted values and maybe also residuals.

The documentation indicates, that I need to use the function modeltime_calibrate() to get the confidence values and residuals. So one question would be, where do I extract the fitted values from?

My main question is whatsoever, how to do calibration on recursive ensembles. For any non-ensemble model I was able to do it, but in case of recursive ensembles I encounter some error messages, if I want to calibrate.

To illustrate the problem, look at the example code below, which ends up failing to calibrate all models:

...

ANSWER

Answered 2021-Dec-01 at 11:13

The problem lies in your recursive_ensemble_panel. You have to do the recursive part on the models themselves and not the ensemble. Like you I would have expected to do the recursive in one go, maybe via modeltime_table.

Source https://stackoverflow.com/questions/70181581

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install modeltime.ensemble

Install the CRAN version:.
Getting Started with Modeltime: Learn the basics of forecasting with Modeltime.
Getting Started with Modeltime Ensemble: Learn the basics of forecasting with Modeltime ensemble models.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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