The R package fable.binary provides a collection of time series forecasting models suitable for binary time series. These models work within the fable framework, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.
You can install the development version from GitHub
# install.packages("remotes")remotes::install_github("tidyverts/fable.binary")library(fable.binary)
#> Loading required package: fabletools
library(ggplot2)
library(dplyr)
#>#> Attaching package: 'dplyr'#> The following objects are masked from 'package:stats':#>#> filter, lag#> The following objects are masked from 'package:base':#>#> intersect, setdiff, setequal, union# Fit modelsfit<-melb_rain|>
model(
nn= BINNET(Wet~ fourier(K=1, period="year")),
logistic= LOGISTIC(Wet~ fourier(K=5, period="year"))
)
# Functions for computing on modelsfit|> tidy()
#> # A tibble: 11 × 6#> .model term estimate std.error statistic p.value#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>#> 1 logistic "(Intercept)" -0.573 0.0321 -17.8 3.18e-71#> 2 logistic "fourier(K = 5, period = \"ye… -0.325 0.0458 -7.10 1.26e-12#> 3 logistic "fourier(K = 5, period = \"ye… -0.279 0.0451 -6.18 6.25e-10#> 4 logistic "fourier(K = 5, period = \"ye… -0.0203 0.0455 -0.446 6.56e- 1#> 5 logistic "fourier(K = 5, period = \"ye… -0.0312 0.0453 -0.688 4.91e- 1#> 6 logistic "fourier(K = 5, period = \"ye… -0.0696 0.0454 -1.53 1.26e- 1#> 7 logistic "fourier(K = 5, period = \"ye… -0.0207 0.0454 -0.457 6.48e- 1#> 8 logistic "fourier(K = 5, period = \"ye… -0.0342 0.0454 -0.754 4.51e- 1#> 9 logistic "fourier(K = 5, period = \"ye… 0.0224 0.0454 0.494 6.21e- 1#> 10 logistic "fourier(K = 5, period = \"ye… -0.0188 0.0453 -0.415 6.78e- 1#> 11 logistic "fourier(K = 5, period = \"ye… 0.00815 0.0453 0.180 8.57e- 1fit|> select(logistic) |> glance()
#> # A tibble: 1 × 12#> .model df log_lik AIC AICc BIC deviance df.residual rank#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>#> 1 logistic 11 -2787. 5596. 5596. 5666. 5574. 4311 11#> # ℹ 3 more variables: null_deviance <dbl>, df_null <int>, nobs <int>fit|> select(logistic) |> report()
#> Series: Wet#> Model: LOGISTIC#>#> Coefficients:#> Estimate Std. Error t value Pr(>|t|)#> (Intercept) -0.573059 0.032114 -17.845 < 2e-16 ***#> fourier(K = 5, period = "year")C1_365 -0.324774 0.045754 -7.098 1.26e-12 ***#> fourier(K = 5, period = "year")S1_365 -0.278737 0.045075 -6.184 6.25e-10 ***#> fourier(K = 5, period = "year")C2_365 -0.020283 0.045519 -0.446 0.656#> fourier(K = 5, period = "year")S2_365 -0.031195 0.045310 -0.688 0.491#> fourier(K = 5, period = "year")C3_365 -0.069556 0.045449 -1.530 0.126#> fourier(K = 5, period = "year")S3_365 -0.020730 0.045374 -0.457 0.648#> fourier(K = 5, period = "year")C4_365 -0.034241 0.045438 -0.754 0.451#> fourier(K = 5, period = "year")S4_365 0.022435 0.045375 0.494 0.621#> fourier(K = 5, period = "year")C5_365 -0.018772 0.045275 -0.415 0.678#> fourier(K = 5, period = "year")S5_365 0.008153 0.045337 0.180 0.857#> ---#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#>#> # A tibble: 1 × 11#> df log_lik AIC AICc BIC deviance df.residual rank null_deviance#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>#> 1 11 -2787. 5596. 5596. 5666. 5574. 4311 11 5668.#> # ℹ 2 more variables: df_null <int>, nobs <int>fit|> select(nn) |> glance()
#> # A tibble: 1 × 6#> .model inputs hidden_nodes weights repeats sigma2#> <chr> <dbl> <dbl> <int> <int> <dbl>#> 1 nn 2 2 9 20 0.227fit|> select(nn) |> report()
#> Series: Wet#> Model: BINNET: 2#>#> Average of 20 networks, each of which is#> a 2-2-1 network with 9 weights#> options were -#>#> sigma^2 estimated as 0.2269
augment(fit)
#> # A tsibble: 8,644 x 6 [1D]#> # Key: .model [2]#> .model Date Wet .fitted .resid .innov#> <chr> <date> <lgl> <dbl> <dbl> <dbl>#> 1 nn 2000-01-01 TRUE 0.255 0.745 0.745#> 2 nn 2000-01-02 FALSE 0.254 -0.254 -0.254#> 3 nn 2000-01-03 FALSE 0.253 -0.253 -0.253#> 4 nn 2000-01-04 TRUE 0.252 0.748 0.748#> 5 nn 2000-01-05 TRUE 0.251 0.749 0.749#> 6 nn 2000-01-06 FALSE 0.250 -0.250 -0.250#> 7 nn 2000-01-07 FALSE 0.249 -0.249 -0.249#> 8 nn 2000-01-08 FALSE 0.248 -0.248 -0.248#> 9 nn 2000-01-09 FALSE 0.248 -0.248 -0.248#> 10 nn 2000-01-10 TRUE 0.247 0.753 0.753#> # ℹ 8,634 more rows# Produce forecasts. For neural network, usefc<- forecast(fit, h="2 years")
as_tibble(fc) |>
ggplot(aes(x=Date, y=.mean, col=.model)) +
geom_line() +
labs(y="Probability of rain")