IntermittentDemand

“IntermittentDemand” is a Julia package that provides functions for forecasting and analyzing intermittent time series, which are time series with long periods of zeros or near-zeros, and occasional non-zero observations.

The package currently includes functions for forecasting intermittent time series using following methods: Croston’s method, Syntetos-Boylan Approximation, Shale-Boylan-Johnston method. Additionally, the package provides API for plotting the implemented methods.

To install “IntermittentDemand”, you can clone the repository directly from GitHub. Open a Julia REPL and enter the following commands:

] # enter package mode
add https://github.com/Akai01/IntermittentDemand.jl.git

After the package is added, you can import the necessary functions into your Julia script or notebook and start using them.

If you have any feedback or suggestions for the package, or if you encounter any issues while using it, please feel free to open an issue on the package’s GitHub repository. We welcome contributions and feedback from the community to improve this package.

Examples

Data

data = [6, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 
0, 0, 0, 0, 0];

Forecasting using Croston method

fc = crost(data = data, h = 12, w = missing, init= "mean", nop = 2, method = "croston", cost = "mar", init_opt = true, na_rm = false);

Plot

using Plots;
plot(fc)

Forecasting using Syntetos-Boylan approximation method

fc = crost(data = data, h = 12, w = missing, init= "mean", nop = 2, method = "sba", cost = "mar", init_opt = true, na_rm = false);

Plot

using Plots;
plot(fc)

Forecasting using Shale-Boylan-Johnston method

fc = crost(data = data, h = 12, w = missing, init= "mean", nop = 2, method = "sbj", cost = "mar", init_opt = true, na_rm = false);

Plot

using Plots;
plot(fc)

References :

• Kourentzes N (2022). tsintermittent: Intermittent Time Series Forecasting. R package version 1.10, https://CRAN.R-project.org/package=tsintermittent.

• Optimisation of the methods described in: N. Kourentzes, 2014, On intermittent demand model optimisation and selection, International Journal of Production Economics, 156: 180-190. https://www.sciencedirect.com/science/article/abs/pii/S092552731400190X?via%3Dihub. https://kourentzes.com/forecasting/2014/06/11/on-intermittent-demand-model-optimisation-and-selection/