https://github.com/lampspuc/statespacemodels.jl

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

Science Score: 23.0%

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Links to: arxiv.org
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Low similarity (13.6%) to scientific vocabulary

Keywords

econometrics exponential-smoothing forecasting julia-language kalman-filter sarima state-space-models statistics time-series time-series-analysis unobserved-components

Keywords from Contributors

conic-programs convex-optimization semidefinite-programming mcmc sciml neural-ode differential-equations mathematical-programming bayesian-inference bayesian-neural-networks

Last synced: 5 months ago · JSON representation

Repository

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

Basic Info

Host: GitHub
Owner: LAMPSPUC
License: mit
Language: Julia
Default Branch: master
Homepage: https://lampspuc.github.io/StateSpaceModels.jl/latest/
Size: 10.5 MB

Statistics

Stars: 277
Watchers: 10
Forks: 28
Open Issues: 16
Releases: 44

Topics

econometrics exponential-smoothing forecasting julia-language kalman-filter sarima state-space-models statistics time-series time-series-analysis unobserved-components

Created about 8 years ago · Last pushed 11 months ago

Metadata Files

Readme License

StateSpaceModels.jl

| Build Status | Coverage | Documentation | |:-----------------:|:-----------------:|:-----------------:| | | |

StateSpaceModels.jl is a package for modeling, forecasting, and simulating time series in a state-space framework. Implementations were made based on the book "Time Series Analysis by State Space Methods" (2012) by James Durbin and Siem Jan Koopman. The notation of the variables in the code also follows the book.

Quickstart

```julia import Pkg

Pkg.add("StateSpaceModels")

using StateSpaceModels

y = randn(100)

model = LocalLevel(y)

fit!(model)

print_results(model)

forecast(model, 10)

kf = kalman_filter(model)

v = get_innovations(kf)

ks = kalman_smoother(model)

alpha = getsmoothedstate(ks) ```

Features

Current features include: * Kalman filter and smoother * Maximum likelihood estimation * Forecasting and Monte Carlo simulation * User-defined models (user specifies the state-space system) * Several predefined models, including: * Exponential Smoothing (ETS, all the linear ones) * Unobserved components (local level, basic structural, ...) * SARIMA * Linear regression * Naive models * Completion of missing values * Diagnostics for the residuals of fitted models * Visualization recipes

Quick Examples

Fitting and forecasting

Quick example of different models fit and forecast for the air passengers time-series

```julia using CSV using DataFrames using Plots using StateSpaceModels

airp = CSV.File(StateSpaceModels.AIRPASSENGERS) |> DataFrame logairpassengers = log.(airp.passengers) stepsahead = 30

SARIMA

modelsarima = SARIMA(logairpassengers; order = (0, 1, 1), seasonalorder = (0, 1, 1, 12)) fit!(modelsarima) forecsarima = forecast(modelsarima, stepsahead)

Unobserved Components

modeluc = UnobservedComponents(logairpassengers; trend = "local linear trend", seasonal = "stochastic 12") fit!(modeluc) forecuc = forecast(modeluc, steps_ahead)

Exponential Smoothing

modelets = ExponentialSmoothing(logairpassengers; trend = true, seasonal = 12) fit!(modelets) forecets = forecast(modelets, steps_ahead)

Naive model

modelnaive = SeasonalNaive(logairpassengers, 12) fit!(modelnaive) forecnaive = forecast(modelnaive, steps_ahead)

pltsarima = plot(modelsarima, forecsarima; title = "SARIMA", label = ""); pltuc = plot(modeluc, forecuc; title = "Unobserved components", label = ""); pltets = plot(modelets, forecets; title = "Exponential smoothing", label = ""); pltnaive = plot(modelets, forecnaive; title = "Seasonal Naive", label = "");

plot(pltsarima, pltuc, pltets, pltnaive; layout = (2, 2), size = (500, 500)) ``` quick_example_airp

Automatic forecasting

Quick examples on automatic forecasting. When performing automatic forecasting users should provide the seasonal period if there is one. julia model = auto_ets(log_air_passengers; seasonal = 12) model = auto_arima(log_air_passengers; seasonal = 12)

Contributing

PRs such as adding new models and fixing bugs are very welcome!
For nontrivial changes, you'll probably want to first discuss the changes via issue.

Citing StateSpaceModels.jl

If you use StateSpaceModels.jl in your work, we kindly ask you to cite the following paper:

@article{SaavedraBodinSouto2019,
  title={StateSpaceModels.jl: a Julia Package for Time-Series Analysis in a State-Space Framework},
  author={Raphael Saavedra and Guilherme Bodin and Mario Souto},
  journal={arXiv preprint arXiv:1908.01757},
  year={2019}
}

Owner

Name: Laboratory of Applied Mathematical Programming and Statistics
Login: LAMPSPUC
Kind: organization

Website: http://www.puc-rio.br/lamps
Repositories: 6
Profile: https://github.com/LAMPSPUC

Laboratory for research and development on mathematical programming (optimization) and statistics. Electrical and Industrial Engineering departments, PUC-Rio.

GitHub Events

Total

Create event: 2
Commit comment event: 4
Release event: 1
Issues event: 2
Watch event: 12
Delete event: 1
Issue comment event: 6
Push event: 13
Pull request event: 2
Fork event: 3

Last Year

Create event: 2
Commit comment event: 4
Release event: 1
Issues event: 2
Watch event: 12
Delete event: 1
Issue comment event: 6
Push event: 13
Pull request event: 2
Fork event: 3

Committers

Last synced: 9 months ago

All Time

Total Commits: 456
Total Committers: 14
Avg Commits per committer: 32.571
Development Distribution Score (DDS): 0.511

Past Year

Commits: 7
Committers: 2
Avg Commits per committer: 3.5
Development Distribution Score (DDS): 0.286

Top Committers

Name	Email	Commits
guilhermebodin	g**s@g**m	223
rsaavedra	r**3@g**m	156
Raphael Saavedra	r**a@e**r	31
Iago Sichinel Chavarry	i**0@g**m	21
Mario Souto	m**o@g**m	14
azev77	a****7	2
andre_ramos	a****7	2
psrcloud	p**d@p**m	1
Patrick Kofod Mogensen	p**n@g**m	1
Marina Dietze	8****a	1
Marina Dietze	3****e	1
Júlio Hoffimann	j**n@g**m	1
Julia TagBot	5****t	1
Brian Groenke	b**8@g**m	1

Committer Domains (Top 20 + Academic)

psr-inc.com: 1 ele.puc-rio.br: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 53
Total pull requests: 66
Average time to close issues: 4 months
Average time to close pull requests: about 1 month
Total issue authors: 20
Total pull request authors: 10
Average comments per issue: 2.62
Average comments per pull request: 1.32
Merged pull requests: 58
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 2
Pull requests: 5
Average time to close issues: N/A
Average time to close pull requests: 1 day
Issue authors: 2
Pull request authors: 2
Average comments per issue: 0.0
Average comments per pull request: 1.8
Merged pull requests: 4
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

guilhermebodin (23)
SupplyChef (4)
waynelapierre (4)
mapi1 (3)
raphaelsaavedra (2)
iagochavarry (2)
dompazz (2)
juliohm (1)
rschwarz (1)
davibarreira (1)
JuliaTagBot (1)
FelixNoessler (1)
dazhwu (1)
jsimons8 (1)
eoteroe (1)

Pull Request Authors

guilhermebodin (52)
iagochavarry (5)
andreramosfdc (4)
raphaelsaavedra (3)
AnaChikashua (2)
bgroenks96 (1)
dietzemarina (1)
azev77 (1)
PaulMainwood (1)
pkofod (1)

Top Labels

Issue Labels

bug (3) good first issue (2) enhancement (1) docs (1)

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- julia 85 total

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 40

juliahub.com: StateSpaceModels

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

Homepage: https://lampspuc.github.io/StateSpaceModels.jl/latest/
Documentation: https://docs.juliahub.com/General/StateSpaceModels/stable/
License: MIT
Latest release: 0.7.0
published 11 months ago

Versions: 40
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 85 Total

Rankings

Stargazers count: 3.3%

Forks count: 6.8%

Dependent repos count: 9.9%

Average: 14.8%

Dependent packages count: 38.9%

Last synced: 6 months ago

Dependencies

.github/workflows/TagBot.yml actions

JuliaRegistries/TagBot v1 composite

.github/workflows/ci.yml actions

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julia-actions/julia-buildpkg v1 composite
julia-actions/julia-processcoverage v1 composite
julia-actions/julia-runtest v1 composite
julia-actions/setup-julia v1 composite

.github/workflows/doc_cleanup.yml actions

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.github/workflows/documentation.yml actions

actions/checkout v2 composite
julia-actions/setup-julia latest composite

https://github.com/lampspuc/statespacemodels.jl

Science Score: 23.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

StateSpaceModels.jl

Quickstart

Features

Quick Examples

Fitting and forecasting

SARIMA

Unobserved Components

Exponential Smoothing

Naive model

Automatic forecasting

Contributing

Citing StateSpaceModels.jl

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

juliahub.com: StateSpaceModels

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Dependencies