https://github.com/pierrenodet/autoarima.jl

Automatic multi-seasonal ARIMA Learning
https://github.com/pierrenodet/autoarima.jl

arima auto-arima forecasting time-series time-series-analysis

Last synced: 1 day ago
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Automatic multi-seasonal ARIMA Learning

• Host: GitHub
• URL: https://github.com/pierrenodet/autoarima.jl
• Owner: pierrenodet
• License: mit
• Created: 2021-04-24T01:12:19.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-05-05T16:47:35.000Z (over 3 years ago)
• Last Synced: 2024-11-16T01:28:56.846Z (about 2 months ago)
• Topics: arima, auto-arima, forecasting, time-series, time-series-analysis
• Language: Julia
• Homepage:
• Size: 192 KB
• Stars: 5
• Watchers: 3
• Forks: 1
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# AutoARIMA.jl

[![Build Status](https://github.com/pierrenodet/AutoARIMA.jl/workflows/CI/badge.svg)](https://github.com/pierrenodet/AutoARIMA.jl/actions?query=workflow%3ACI)

[![codecov.io](https://codecov.io/github/pierrenodet/AutoARIMA.jl/branch/main/graph/badge.svg)](http://codecov.io/github/pierrenodet/AutoARIMA.jl/branch/main)

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

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

Automatic multi-seasonal ARIMA Learning with Box and Jenkins method.

```julia

using AutoARIMA

julia> z = seriesG

144-element StaticArrays.SVector{144, Int64} with indices SOneTo(144):

 112

 118

 132

 129

   ⋮

 508

 461

 390

 432

julia> sarima = fit(MSARIMAParams([0,0],[1,1],[1,1],[1,12]),log.(z))

ARMAModel{13, 13, Float64}(-0.002232109177566169, [1.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 1.0, -1.0], [0.34919600156185515, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, 0.31610711882734377, -0.11038334195974667], Float64[], 0.0016285014276129083)

julia> forecast(sarima,log.(z))

6.104360295754863

julia> best = boxjenkins(z,true,2,2,2,[1,12],criterium=aicc)

AutoARIMA.ARMAXModel{25, 0, 0, Float64}(-0.0009527614685900121, [1.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0  …  -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -0.0, -1.0, 1.0], Float64[], Float64[], 0.004332904404300882)

```