forecast v8.9
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Forecasting Functions for Time Series and Linear Models
Methods and tools for displaying and analysing
univariate time series forecasts including exponential smoothing
via state space models and automatic ARIMA modelling.
Readme
forecast 
The R package forecast provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
Installation
You can install the stable version from CRAN.
install.packages('forecast', dependencies = TRUE)
You can install the development version from Github
# install.packages("devtools")
devtools::install_github("robjhyndman/forecast")
Usage
library(forecast)
library(ggplot2)
# ETS forecasts
USAccDeaths %>%
ets() %>%
forecast() %>%
autoplot()
# Automatic ARIMA forecasts
WWWusage %>%
auto.arima() %>%
forecast(h=20) %>%
autoplot()
# ARFIMA forecasts
library(fracdiff)
x <- fracdiff.sim( 100, ma=-.4, d=.3)$series
arfima(x) %>%
forecast(h=30) %>%
autoplot()
# Forecasting with STL
USAccDeaths %>%
stlm(modelfunction=ar) %>%
forecast(h=36) %>%
autoplot()
AirPassengers %>%
stlf(lambda=0) %>%
autoplot()
USAccDeaths %>%
stl(s.window='periodic') %>%
forecast() %>%
autoplot()
# TBATS forecasts
USAccDeaths %>%
tbats() %>%
forecast() %>%
autoplot()
taylor %>%
tbats() %>%
forecast() %>%
autoplot()
For more information
- Get started in forecasting with the online textbook at http://OTexts.org/fpp2/
- Read the Hyndsight blog at https://robjhyndman.com/hyndsight/
- Ask forecasting questions on http://stats.stackexchange.com/tags/forecasting
- Ask R questions on http://stackoverflow.com/tags/forecasting+r
- Join the International Institute of Forecasters: http://forecasters.org/
License
This package is free and open source software, licensed under GPL-3.
Functions in forecast
Name | Description | |
auto.arima | Fit best ARIMA model to univariate time series | |
dshw | Double-Seasonal Holt-Winters Forecasting | |
Acf | (Partial) Autocorrelation and Cross-Correlation Function Estimation | |
bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
bizdays | Number of trading days in each season | |
autolayer.mts | Automatically create a ggplot for time series objects | |
baggedModel | Forecasting using a bagged model | |
fitted.fracdiff | h-step in-sample forecasts for time series models. | |
forecast-package | Forecasting Functions for Time Series and Linear Models | |
Arima | Fit ARIMA model to univariate time series | |
dm.test | Diebold-Mariano test for predictive accuracy | |
gglagplot | Time series lag ggplots | |
ggmonthplot | Create a seasonal subseries ggplot | |
croston | Forecasts for intermittent demand using Croston's method | |
forecast.stl | Forecasting using stl objects | |
forecast.bats | Forecasting using BATS and TBATS models | |
forecast.baggedModel | Forecasting using a bagged model | |
fourier | Fourier terms for modelling seasonality | |
mstl | Multiple seasonal decomposition | |
rwf | Naive and Random Walk Forecasts | |
na.interp | Interpolate missing values in a time series | |
plot.Arima | Plot characteristic roots from ARIMA model | |
easter | Easter holidays in each season | |
ndiffs | Number of differences required for a stationary series | |
nnetar | Neural Network Time Series Forecasts | |
msts | Multi-Seasonal Time Series | |
tbats.components | Extract components of a TBATS model | |
residuals.forecast | Residuals for various time series models | |
seasadj | Seasonal adjustment | |
forecast.mts | Forecasting time series | |
plot.bats | Plot components from BATS model | |
forecast.nnetar | Forecasting using neural network models | |
thetaf | Theta method forecast | |
CVar | k-fold Cross-Validation applied to an autoregressive model | |
bld.mbb.bootstrap | Box-Cox and Loess-based decomposition bootstrap. | |
CV | Cross-validation statistic | |
checkresiduals | Check that residuals from a time series model look like white noise | |
sindexf | Forecast seasonal index | |
tsclean | Identify and replace outliers and missing values in a time series | |
tsCV | Time series cross-validation | |
simulate.ets | Simulation from a time series model | |
forecast | Forecasting time series | |
forecast.fracdiff | Forecasting using ARIMA or ARFIMA models | |
forecast.StructTS | Forecasting using Structural Time Series models | |
forecast.HoltWinters | Forecasting using Holt-Winters objects | |
plot.ets | Plot components from ETS model | |
woolyrnq | Quarterly production of woollen yarn in Australia | |
getResponse | Get response variable from time series model. | |
plot.forecast | Forecast plot | |
subset.ts | Subsetting a time series | |
splinef | Cubic Spline Forecast | |
gghistogram | Histogram with optional normal and kernel density functions | |
modelAR | Time Series Forecasts with a user-defined model | |
monthdays | Number of days in each season | |
autoplot.mforecast | Multivariate forecast plot | |
seasonaldummy | Seasonal dummy variables | |
reexports | Objects exported from other packages | |
taylor | Half-hourly electricity demand | |
seasonal | Extract components from a time series decomposition | |
BoxCox | Box Cox Transformation | |
BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
accuracy | Accuracy measures for a forecast model | |
tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
arfima | Fit a fractionally differenced ARFIMA model | |
findfrequency | Find dominant frequency of a time series | |
ets | Exponential smoothing state space model | |
forecast.mlm | Forecast a multiple linear model with possible time series components | |
gas | Australian monthly gas production | |
is.acf | Is an object a particular model type? | |
nsdiffs | Number of differences required for a seasonally stationary series | |
ggseasonplot | Seasonal plot | |
is.forecast | Is an object a particular forecast type? | |
ocsb.test | Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots | |
StatForecast | Forecast plot | |
forecast.modelAR | Forecasting using user-defined model | |
autoplot.acf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting | |
ses | Exponential smoothing forecasts | |
autoplot.decomposed.ts | Plot time series decomposition components using ggplot | |
ggtsdisplay | Time series display | |
tslm | Fit a linear model with time series components | |
forecast.lm | Forecast a linear model with possible time series components | |
forecast.ets | Forecasting using ETS models | |
is.constant | Is an object constant? | |
gold | Daily morning gold prices | |
ma | Moving-average smoothing | |
tsoutliers | Identify and replace outliers in a time series | |
meanf | Mean Forecast | |
wineind | Australian total wine sales | |
autolayer | Create a ggplot layer appropriate to a particular data type | |
arima.errors | Errors from a regression model with ARIMA errors | |
arimaorder | Return the order of an ARIMA or ARFIMA model | |
No Results! |
Vignettes of forecast
Name | ||
JSS-paper.bib | ||
JSS2008.Rmd | ||
jsslogo.jpg | ||
No Results! |
Last month downloads
Details
LinkingTo | Rcpp (>= 0.11.0), RcppArmadillo (>= 0.2.35) |
LazyData | yes |
ByteCompile | TRUE |
BugReports | https://github.com/robjhyndman/forecast/issues |
License | GPL-3 |
URL | http://pkg.robjhyndman.com/forecast, https://github.com/robjhyndman/forecast |
VignetteBuilder | knitr |
Encoding | UTF-8 |
RoxygenNote | 6.1.1 |
NeedsCompilation | yes |
Packaged | 2019-08-22 03:02:51 UTC; hyndman |
Repository | CRAN |
Date/Publication | 2019-08-22 11:50:02 UTC |
depends | , R (>= 3.0.2) |
imports | colorspace , fracdiff , ggplot2 (>= 2.2.1) , graphics , lmtest , magrittr , nnet , parallel , Rcpp (>= 0.11.0) , stats , timeDate , tseries , urca , zoo |
suggests | knitr , methods , rmarkdown , rticles , testthat , uroot |
linkingto | RcppArmadillo (>= 0.2.35) |
Contributors | Earo Wang, Leanne Chhay, Fotios Petropoulos, R Core team, Yuan Tang, Christoph Bergmeir, Slava Razbash, David Shaub, Zhenyu Zhou, Ross Ihaka, Mitchell O'Hara-Wild, George Athanasopoulos, Gabriel Caceres, Farah Yasmeen, Daniel Reid |
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