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tjdc: R package for Trends and Jumps in (flat) DataCubes

Purpose

This R-package accompanies the research paper

T Rajala, P Packalen, M Myllymäki, A Kangas (2023): Improving detection of changepoints in short and noisy time-series with local correlations: Connecting the events in pixel neighbourhoods, Journal of Agricultural, Biological and Environmental Statistics, https://doi.org/10.1007/s13253-023-00546-1

and implements the (proof-of-concept) Gibbs sampler for the described model.

The described model was developed because no ready made solution was found for the required analysis; see below.

Installation

In addition to CRAN dependencies, install the looptimer package.

# 
devtools::install_github("antiphon/looptimer")

# install.packages("doParallel", "foreach", "sf", "stars", "dplyr", "tidyr"))
devtools::install_github("antiphon/tjdc")

This package has a vignette, so consider using build_vignettes = TRUE. The vignette is also readable at the package site,

Input data

Consider a timeseries data, stored as a vector yi = (yi1,...,yiT) of values yit ∈ R recorded at times t1 < ... < tT.

Now consider that each timeseries yi is recorded on the nodes of (spatial) grid. Such as grid-time data array can be called a datacube: Dimension x-y of the cuboid describe location, and z describes time.

See the example datasets

data(package = "tjdc")

data("case_medium_zoom1")
data("case_medium_zoom1_details")
data("easystripes")

The datasets analysed in the original paper are available at Zenodo, https://zenodo.org/record/8009800 .

The model

The package, or more specifically the model it implements, is designed to analyse datacubes assuming

  1. We want to know if, and when, there is a radical change in the mean of each yi
  2. The series are short, T ≪ 100, like T = 11 in the paper’s main example
  3. The unstructed variability (noise) is relatively high, e.g. CV ≫ 5%
  4. The changepoint events are spatially correlated, so taking place in “patches” of locations

Functionality

The package provides wrappers for

  1. Running the Gibbs sampler
  2. Parallelising: Splitting the datacube spatially with minor overlap, parallel running the model, and then gathering the posteriors

The main fitting function is tjdc::tj_fit_m0.6, proceed there.

For large problems it is recommended to use the file-storage options (see tjdc::tj_fit_m0.6).