Package: tsfknn 0.6.0
tsfknn: Time Series Forecasting Using Nearest Neighbors
Allows forecasting time series using nearest neighbors regression Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J. Rivera (2019) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. The nearest neighbors used in a prediction can be consulted and plotted.
Authors:
tsfknn_0.6.0.tar.gz
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tsfknn_0.6.0.tgz(r-4.4-x86_64)tsfknn_0.6.0.tgz(r-4.4-arm64)tsfknn_0.6.0.tgz(r-4.3-x86_64)tsfknn_0.6.0.tgz(r-4.3-arm64)
tsfknn_0.6.0.tar.gz(r-4.5-noble)tsfknn_0.6.0.tar.gz(r-4.4-noble)
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tsfknn.pdf |tsfknn.html✨
tsfknn/json (API)
NEWS
| # Install 'tsfknn' in R: |
| install.packages('tsfknn', repos = c('https://franciscomartinezdelrio.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/franciscomartinezdelrio/tsfknn/issues
Last updated 11 months agofrom:45e95c28ac. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 07 2024 |
| R-4.5-win-x86_64 | OK | Nov 07 2024 |
| R-4.5-linux-x86_64 | OK | Nov 07 2024 |
| R-4.4-win-x86_64 | OK | Nov 07 2024 |
| R-4.4-mac-x86_64 | OK | Nov 07 2024 |
| R-4.4-mac-aarch64 | OK | Nov 07 2024 |
| R-4.3-win-x86_64 | OK | Nov 07 2024 |
| R-4.3-mac-x86_64 | OK | Nov 07 2024 |
| R-4.3-mac-aarch64 | OK | Nov 07 2024 |
Exports:knn_examplesknn_forecastingn_training_examplesnearest_neighborsrolling_origin
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcpprlangscalestibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create a ggplot object from a knnForecast object | autoplot.knnForecast |
| Examples of the model associated with a prediction | knn_examples |
| Time series forecasting using KNN regression | knn_forecasting |
| Number of training examples | n_training_examples |
| Nearest neighbors associated with predictions | nearest_neighbors |
| Plot a prediction of a test set | plot.knnForecastRO |
| Predict method for KNN models for time series forecasting. | predict.knnForecast |
| Assessing forecasting accuracy with rolling origin | rolling_origin |
