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https://github.com/earowang/hts
Hierarchical and Grouped Time Series
https://github.com/earowang/hts
cran forecasts hts r
Last synced: 7 days ago
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Hierarchical and Grouped Time Series
- Host: GitHub
- URL: https://github.com/earowang/hts
- Owner: earowang
- Created: 2013-11-22T00:07:17.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2024-07-30T12:58:55.000Z (3 months ago)
- Last Synced: 2024-10-15T23:36:49.383Z (22 days ago)
- Topics: cran, forecasts, hts, r
- Language: R
- Homepage: http://pkg.earo.me/hts
- Size: 8.08 MB
- Stars: 110
- Watchers: 14
- Forks: 36
- Open Issues: 20
-
Metadata Files:
- Readme: README.Rmd
- Contributing: .github/CONTRIBUTING.md
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
---
output:
github_document:
html_preview: false
---
```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.path = "man/figures/")
```
# hts 
[](https://github.com/earowang/hts/actions?workflow=R-CMD-check)
[](https://cran.r-project.org/package=hts)
[](https://cran.r-project.org/package=hts)
[](https://lifecycle.r-lib.org/articles/stages.html)
**hts** is retired, with minimum maintenance to keep it on CRAN. We recommend using the [fable](http://fable.tidyverts.org) package instead.
The R package *hts* presents functions to create, plot and forecast hierarchical and grouped time series.
## Installation
You can install the **stable** version on
[R CRAN](https://cran.r-project.org/package=hts).
```r
install.packages('hts', dependencies = TRUE)
```
You can also install the **development** version from
[Github](https://github.com/earowang/hts)
```r
# install.packages("devtools")
devtools::install_github("earowang/hts")
```
## Usage
### Example 1: hierarchical time series
```{r hts-eg1, echo = TRUE}
library(hts)
# hts example 1
print(htseg1)
summary(htseg1)
aggts1 <- aggts(htseg1)
aggts2 <- aggts(htseg1, levels = 1)
aggts3 <- aggts(htseg1, levels = c(0, 2))
plot(htseg1, levels = 1)
smatrix(htseg1) # Return the dense mode
# Forecasts
fcasts1.bu <- forecast(
htseg1, h = 4, method = "bu", fmethod = "ets", parallel = TRUE
)
aggts4 <- aggts(fcasts1.bu)
summary(fcasts1.bu)
fcasts1.td <- forecast(
htseg1, h = 4, method = "tdfp", fmethod = "arima", keep.fitted = TRUE
)
summary(fcasts1.td) # When keep.fitted = TRUE, return in-sample accuracy
fcasts1.comb <- forecast(
htseg1, h = 4, method = "comb", fmethod = "ets", keep.fitted = TRUE
)
aggts4 <- aggts(fcasts1.comb)
plot(fcasts1.comb, levels = 2)
plot(fcasts1.comb, include = 5, levels = c(1, 2))
```
### Example 2: hierarchical time series
```{r hts-eg2, echo = TRUE}
# hts example 2
data <- window(htseg2, start = 1992, end = 2002)
test <- window(htseg2, start = 2003)
fcasts2.mo <- forecast(
data, h = 5, method = "mo", fmethod = "ets", level = 1,
keep.fitted = TRUE, keep.resid = TRUE
)
accuracy.gts(fcasts2.mo, test)
accuracy.gts(fcasts2.mo, test, levels = 1)
fcasts2.td <- forecast(
data, h = 5, method = "tdgsa", fmethod = "ets",
keep.fitted = TRUE, keep.resid = TRUE
)
plot(fcasts2.td, include = 5)
plot(fcasts2.td, include = 5, levels = c(0, 2))
```
### Example 3: grouped time series
```{r gts-eg, echo = TRUE}
# gts example
plot(infantgts, levels = 1)
fcasts3.comb <- forecast(infantgts, h = 4, method = "comb", fmethod = "ets")
agg_gts1 <- aggts(fcasts3.comb, levels = 1)
agg_gts2 <- aggts(fcasts3.comb, levels = 1, forecasts = FALSE)
plot(fcasts3.comb)
plot(fcasts3.comb, include = 5, levels = c(1, 2))
fcasts3.combsd <- forecast(
infantgts, h = 4, method = "comb", fmethod = "ets",
weights = "sd", keep.fitted = TRUE
)
fcasts3.combn <- forecast(
infantgts, h = 4, method = "comb", fmethod = "ets",
weights = "nseries", keep.resid = TRUE
)
```
## License
This package is free and open source software, licensed under GPL (>= 2).