Telescope

Telescope is a machine learningbased forecasting approach that automatically retrieves relevant information from a given time series..

Details of the Telescope approach can be found at [1,2]. The details of the recommendation approach can be found at [3].

Installation

This package can be installed in R by using the following commands:

install.packages("devtools")
devtools::install_github("DescartesResearch/telescope")

For unknown reasons, install_github does not work under all Windows versions. Therefore the package can alternatively be installed in R with the following commands:

install.packages("remotes")
remotes::install_url(url="https://github.com/DescartesResearch/telescope/archive/master.zip", INSTALL_opt= "--no-multiarch")

Example without Recommendition system

library(telescope)
forecast <- telescope.forecast(forecast::taylor, horizon = 1000)

Example with Recommendition system

install.packages('Mcomp')
library(Mcomp)
library(telescope)

ts.list <- list()
for(i in 1:length(M3)){
  ts.list[i] <- list(ts(c(M3[[i]]$x,M3[[i]]$xx),frequency = frequency(M3[[i]]$x)))` <br />
}

model <- telescope.trainrecommender(ts.list)
telescope.forecast(forecast::taylor, horizon = 1000, rec_model = model)

Further Information and References

For more information on this forecasting method, please visit our homepage.

[1] Bauer, A., Züfle, M., Herbst, N., Kounev, S. & Curtef, V. (2020). Telescope: An Automatic Feature Extraction and Transformation Approach for Time Series Forecasting on a Level-Playing Field. Proceedings of the 36th International Conference on Data Engineering (ICDE) (p./pp. 1902-1905).

[2] Bauer, A., Züfle, M., Herbst, N., Zehe, A., Hotho, A. & Kounev, S. (2020). Time Series Forecasting for Self-Aware Systems. Proceedings of the IEEE.

[3] Bauer, A., Züfle, M., Grohmann, J., Schmitt, N., Herbst, N. & Kounev, S. (2020). An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series. Proceedings of the 2020 ACM/SPEC International Conference on Performance Engineering (p./pp. 48-55), April, New York, NY, USA: ACM.