https://github.com/jszitas/soothsayer
Automatic Time Series Forecasting and Ensembling via Meta-learning
https://github.com/jszitas/soothsayer
forecasting forecasting-models machine-learning meta-learning model-selection r time-series
Last synced: 8 months ago
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Automatic Time Series Forecasting and Ensembling via Meta-learning
- Host: GitHub
- URL: https://github.com/jszitas/soothsayer
- Owner: JSzitas
- License: other
- Created: 2021-10-22T20:48:01.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2023-04-11T16:41:42.000Z (about 3 years ago)
- Last Synced: 2025-01-21T14:45:42.570Z (over 1 year ago)
- Topics: forecasting, forecasting-models, machine-learning, meta-learning, model-selection, r, time-series
- Language: R
- Homepage:
- Size: 48.9 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 6
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
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README
---
output: github_document
always_allow_html: true
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# soothsayer
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://github.com/JSzitas/soothsayer/actions)
[](https://codecov.io/gh/JSzitas/soothsayer?branch=main)
[](https://CRAN.R-project.org/package=soothsayer)
**soothsayer** provides tools for automation of time series model selection.
This is achieved by fitting your own version of Feature-based Forecast Model Selection (FFORMS), as described in .
The package does this in a way that tries to leverage the **tidy** framework.
## Workflow
* compute time series features (e.g. Box-Pierce statistic, time series entropy, ...)
* run a **set of rules**, or an **oracle** model for fitting models
* generate forecast combinations
The forecast rules are implemented directly in the model, i.e. the following is perfectly valid and runnable code:
```{r}
library(fabletools)
library(soothsayer)
ex_data <- tsibbledata::aus_livestock %>%
as.data.frame() %>%
dplyr::group_by(Month, State) %>%
dplyr::summarise(count = sum(Count)) %>%
dplyr::ungroup() %>%
tsibble::as_tsibble(index = "Month", key= "State")
train <- dplyr::filter(ex_data, Month <= tsibble::yearmonth("2017 Jan"))
test <- dplyr::filter( ex_data, Month > tsibble::yearmonth("2017 Jan") )
mdls <- fabletools::model(
train,
ar = fable::ARIMA( count ),
arima = fable::ARIMA(count),
soothsayer = soothsayer(count ~
# only fit arima if there is over 12 observations
rules(arima ~ .length > 12,
# always fit an AR model
ar ~ TRUE,
# only fit ets if length is over 15
ets ~ .length > 15))
)
```
Note that this works seamlessly with fitting any model in the **fable** package
and models which work in the **fabletools** framework. This includes, among others
[fable.tbats](https://github.com/JSzitas/fable.tbats), the wrapper for the TBATS
model.
This then works just like any other model in **fable**, and generating forecasts
is seamless:
```{r}
forecast(mdls, test) %>%
# only a subset of forecasts plotted for visibility
dplyr::filter(State == "Western Australia") %>%
ggplot2::autoplot(test)
```
# Combinations
**soothsayer** also supports forecast combinations. Following the example above,
by default, soothsayer creates an equally weighed combination of all models
which are matched:
```{r}
# this is identical to the above
mdls <- fabletools::model(
train,
ar = fable::ARIMA( count ),
arima = fable::ARIMA(count),
soothsayer = soothsayer(count ~
rules(arima ~ .length > 12,
ar ~ TRUE,
ets ~ .length > 15) +
# note the explicit combiner, though
combiner(combiner_equal))
)
```
However, multiple combiners are implemented within the package -
a good example being the **'greedy stacking'** one (See [1]):
```{r}
# this is identical to the above
mdls <- fabletools::model(
train,
ar = fable::ARIMA( count ),
arima = fable::ARIMA(count),
soothsayer = soothsayer(count ~
rules(arima ~ .length > 12,
ar ~ TRUE,
ets ~ .length > 15) +
# note the explicit combiner, though
combiner(combiner_greedy_stacking))
)
```
These allow you to implement specific types of weighed combinations, including
preset weights (**combiner_custom**). Further, note that the combiner argument
takes a function - any function with the following signature:
```{r}
new_combiner <- function(.models, ...) {
# your code goes here
}
```
this is expected to return a vector of weights in the same order as **.models**,
where **.models** is the fitted model *mable** object.
# Adding soothsayers to existing mables
TODO: Document
# References
+ [1] Kurz, Maier & Rink; 'A greedy stacking algorithm for model ensembling and domain weighting',
BMC Research Notes volume 13, Article number: 70 (2020)
(See full text at https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-020-4931-7)