dejavu

The R package dejavu provides efficient algorithms for forecasting with Similarity of a bunch of time series.

Installation

You can install dejavu package from GitHub Repository with:

devtools::install_github("kl-lab/dejavu")

Usage

Load the package

require("dejavu")

Forecasting with Similarity

Using Reference data from M3 Competition (Download the repository to local first)

• First download the reference data from GitHub to a folder e.g. workspace. Note that the reference git repository is git-lfs controlled, please use a recent version of git and git-lfs software to clone it.

  cd workspace
  git clone https://github.com/kl-lab/dejavu-refdata.git

• Now within R, you switch the work directory to workspace

  setwd('workspace')
  library(dtw)
  library(robustbase)
  library(forecast)
  fc_Simi <- Similarity(AirPassengers, fh = 20, LoadData = TRUE, path = NULL)
  fcs_result <- ts(fc_Simi$fcs, start = 1961, frequency = 12)
  PIL_result <- ts(fc_Simi$PIL, start = 1961, frequency = 12)
  PIU_result <- ts(fc_Simi$PIU, start = 1961, frequency = 12)
  autoplot(AirPassengers)+autolayer(fcs_result)+autolayer(PIL_result)+autolayer(PIU_result)

Using user-defined reference data

Assume you have created a user-defined reference in the folder my-dejavu-refdata

fc_Simi <- Similarity(AirPassengers, fh = 20, LoadData = FALSE, path = "my-dejavu-refdata")

References

• Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li, Vassilios Assimakopoulos (2020). Déjà vu: A data-centric forecasting approach through time series cross-similarity Journal of Business Research. Working paper on arXiv.

License

This package is free and open source software, licensed under GPL-3.