forecast v8.4
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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 | |
autolayer.mts | Automatically create a ggplot for time series objects | |
forecast.mts | Forecasting time series | |
is.constant | Is an object constant? | |
ocsb.test | Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots | |
forecast.mlm | Forecast a multiple linear model with possible time series components | |
Arima | Fit ARIMA model to univariate time series | |
forecast | Forecasting time series | |
gold | Daily morning gold prices | |
ma | Moving-average smoothing | |
forecast.modelAR | Forecasting using user-defined model | |
nsdiffs | Number of differences required for a seasonally stationary series | |
autoplot.decomposed.ts | Plot time series decomposition components using ggplot | |
seasonal | Extract components from a time series decomposition | |
meanf | Mean Forecast | |
tslm | Fit a linear model with time series components | |
autoplot.acf | ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting | |
ggtsdisplay | Time series display | |
seasonaldummy | Seasonal dummy variables | |
rwf | Naive and Random Walk Forecasts | |
ets | Exponential smoothing state space model | |
fitted.fracdiff | h-step in-sample forecasts for time series models. | |
CV | Cross-validation statistic | |
forecast-package | Forecasting Functions for Time Series and Linear Models | |
is.acf | Is an object a particular model type? | |
mstl | Multiple seasonal decomposition | |
autolayer | Create a ggplot layer appropriate to a particular data type | |
forecast.StructTS | Forecasting using Structural Time Series models | |
taylor | Half-hourly electricity demand | |
croston | Forecasts for intermittent demand using Croston's method | |
autoplot.mforecast | Multivariate forecast plot | |
tbats | TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
fourier | Fourier terms for modelling seasonality | |
forecast.nnetar | Forecasting using neural network models | |
findfrequency | Find dominant frequency of a time series | |
getResponse | Get response variable from time series model. | |
na.interp | Interpolate missing values in a time series | |
dm.test | Diebold-Mariano test for predictive accuracy | |
CVar | k-fold Cross-Validation applied to an autoregressive model | |
tsoutliers | Identify and replace outliers in a time series | |
is.forecast | Is an object a particular forecast type? | |
forecast.ets | Forecasting using ETS models | |
reexports | Objects exported from other packages | |
ses | Exponential smoothing forecasts | |
gghistogram | Histogram with optional normal and kernel density functions | |
ndiffs | Number of differences required for a stationary series | |
seasadj | Seasonal adjustment | |
nnetar | Neural Network Time Series Forecasts | |
forecast.baggedModel | Forecasting using a bagged model | |
baggedModel | Forecasting using a bagged model | |
forecast.bats | Forecasting using BATS and TBATS models | |
ggseasonplot | Seasonal plot | |
dshw | Double-Seasonal Holt-Winters Forecasting | |
residuals.forecast | Residuals for various time series models | |
easter | Easter holidays in each season | |
tsclean | Identify and replace outliers and missing values in a time series | |
simulate.ets | Simulation from a time series model | |
gglagplot | Time series lag ggplots | |
plot.forecast | Forecast plot | |
forecast.lm | Forecast a linear model with possible time series components | |
gas | Australian monthly gas production | |
subset.ts | Subsetting a time series | |
plot.ets | Plot components from ETS model | |
StatForecast | Forecast plot | |
monthdays | Number of days in each season | |
ggmonthplot | Create a seasonal subseries ggplot | |
tsCV | Time series cross-validation | |
wineind | Australian total wine sales | |
woolyrnq | Quarterly production of woollen yarn in Australia | |
plot.Arima | Plot characteristic roots from ARIMA model | |
msts | Multi-Seasonal Time Series | |
sindexf | Forecast seasonal index | |
plot.bats | Plot components from BATS model | |
thetaf | Theta method forecast | |
tbats.components | Extract components of a TBATS model | |
arima.errors | Errors from a regression model with ARIMA errors | |
arimaorder | Return the order of an ARIMA or ARFIMA model | |
bats | BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components) | |
forecast.fracdiff | Forecasting using ARIMA or ARFIMA models | |
bizdays | Number of trading days in each season | |
checkresiduals | Check that residuals from a time series model look like white noise | |
accuracy | Accuracy measures for a forecast model | |
bld.mbb.bootstrap | Box-Cox and Loess-based decomposition bootstrap. | |
BoxCox | Box Cox Transformation | |
Acf | (Partial) Autocorrelation and Cross-Correlation Function Estimation | |
forecast.HoltWinters | Forecasting using Holt-Winters objects | |
arfima | Fit a fractionally differenced ARFIMA model | |
auto.arima | Fit best ARIMA model to univariate time series | |
modelAR | Time Series Forecasts with a user-defined model | |
BoxCox.lambda | Automatic selection of Box Cox transformation parameter | |
forecast.stl | Forecasting using stl objects | |
splinef | Cubic Spline Forecast | |
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 |
RoxygenNote | 6.0.1.9000 |
NeedsCompilation | yes |
Packaged | 2018-06-21 04:53:37 UTC; mitchell |
Repository | CRAN |
Date/Publication | 2018-06-21 13:59:21 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 , uroot , zoo |
suggests | expsmooth , knitr , rmarkdown , rticles , testthat |
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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