https://github.com/akai01/intermittentdemand.jl

IntermittentDemand.jl: Intermittent demand forecasting in Julia Language, |forecasting|Julia|
https://github.com/akai01/intermittentdemand.jl

forecasting intermittent-demand inventory-management julia julia-language lumpy-time-series operations-research statistical-models statistics time-series

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IntermittentDemand.jl: Intermittent demand forecasting in Julia Language, |forecasting|Julia|

• Host: GitHub
• URL: https://github.com/akai01/intermittentdemand.jl
• Owner: Akai01
• License: mit
• Created: 2023-01-25T10:24:17.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-02-18T10:21:21.000Z (over 1 year ago)
• Last Synced: 2024-01-30T15:14:51.254Z (9 months ago)
• Topics: forecasting, intermittent-demand, inventory-management, julia, julia-language, lumpy-time-series, operations-research, statistical-models, statistics, time-series
• Language: Julia
• Homepage:
• Size: 125 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# IntermittentDemand

[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://akai01.github.io/IntermittentDemand.jl/stable/)

[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://akai01.github.io/IntermittentDemand.jl/dev/)

[![Build Status](https://github.com/akai01/IntermittentDemand.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/akai01/IntermittentDemand.jl/actions/workflows/CI.yml?query=branch%3Amain)



“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, .

* Optimisation of the methods described in:

N. Kourentzes, 2014, On intermittent demand model optimisation and selection, International Journal of Production Economics, 156: 180-190. .