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Durbyn.jl

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Durbyn is a Julia package that implements functionality of the R forecast package, providing tools for time-series forecasting.

The name Durbyn traces back to Kurdish, where Dur means “far” and Byn means “to see,” together signifying binoculars, which is why the package logo features them.

This site documents the development version. After your first tagged release, see stable docs for the latest release.

About TAFS

TAFS (Time Series Analysis and Forecasting Society) is a non-profit association (“Verein”) in Vienna, Austria. It connects academics, experts, practitioners, and students focused on time-series, forecasting, and decision science. Contributions remain fully open source. Learn more at taf-society.org.

Installation

Durbyn is under active development. For the latest dev version:

using Pkg
Pkg.add(url="https://github.com/taf-society/Durbyn.jl")

Quick peek (ETS)

using Durbyn
using Durbyn.ExponentialSmoothing

ap = air_passengers()

fit_ets = ets(ap, 12, "ZZZ")
fc_ets  = forecast(fit_ets, h = 12)
plot(fc_ets)

ses_fit = ses(ap, 12)
ses_fc  = forecast(ses_fit, h = 12)
plot(ses_fc)

holt_fit = holt(ap, 12)
holt_fc  = forecast(holt_fit, h = 12)
plot(holt_fc)

hw_fit = holt_winters(ap, 12)
hw_fc  = forecast(hw_fit, h = 12)
plot(hw_fc)

Intermittent demand (Croston variants)

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];

# Based on Shenstone & Hyndman (2005)
m = 1
fit_crst = croston(data, m)
fc_crst  = forecast(fit_crst, 12)
plot(fc_crst)

using Durbyn.IntermittentDemand

# Classical Croston (Croston, 1972)
crst1 = croston_classic(data)
fc1   = forecast(crst1, h = 12)

residuals(crst1); residuals(fc1);
fitted(crst1);    fitted(fc1);
plot(fc1, show_fitted = true)

# Croston + SBA correction
crst2 = croston_sba(data)
fc2   = forecast(crst2, h = 12)
plot(fc2, show_fitted = true)

# Croston + SBJ correction
crst3 = croston_sbj(data)
fc3   = forecast(crst3, h = 12)
plot(fc3, show_fitted = true)

ARIMA

using Durbyn.Arima

ap  = air_passengers()

# manual ARIMA
fit = arima(ap, 12, order = PDQ(2,1,1), seasonal = PDQ(0,1,0))
fc  = forecast(fit, h = 12)

# auto ARIMA
fit2 = auto_arima(ap, 12, d = 1, D = 1)
fc2  = forecast(fit2, h = 12)
plot(fc2)

ARAR / ARARMA

using Durbyn
using Durbyn.Ararma

ap = air_passengers()

fit  = arar(ap, max_ar_depth = 13)
fc   = forecast(fit, h = 12)
plot(fc)

fit2 = ararma(ap, p = 0, q = 1)
fc2  = forecast(fit2, h = 12)
plot(fc2)

fit3 = auto_ararma(ap)
fc3  = forecast(fit3, h = 12)
plot(fc3)

License

MIT License.

What’s next

  • Read the Quick Start (left sidebar).
  • Explore User Guide pages (ETS, Intermittent Demand, ARIMA, ARAR/ARARMA).
  • See the API Reference for full docs.