forecast8.23.0 package

Forecasting Functions for Time Series and Linear Models

Get response variable from time series model.

Histogram with optional normal and kernel density functions

Extract components of a TBATS model

TBATS model (Exponential smoothing state space model with Box-Cox tran...

Accuracy measures for a forecast model

(Partial) Autocorrelation and Cross-Correlation Function Estimation

Fit a fractionally differenced ARFIMA model

Errors from a regression model with ARIMA errors

Fit ARIMA model to univariate time series

Return the order of an ARIMA or ARFIMA model

Fit best ARIMA model to univariate time series

Create a ggplot layer appropriate to a particular data type

Identify and replace outliers in a time series

ggplot (Partial) Autocorrelation and Cross-Correlation Function Estima...

Plot time series decomposition components using ggplot

Automatically create a ggplot for time series objects

Forecasting using a bagged model

BATS model (Exponential smoothing state space model with Box-Cox trans...

Number of trading days in each season

Box-Cox and Loess-based decomposition bootstrap.

Number of days in each season

Automatic selection of Box Cox transformation parameter

Box Cox Transformation

Check that residuals from a time series model look like white noise

Forecasts for intermittent demand using Croston's method

Cross-validation statistic

k-fold Cross-Validation applied to an autoregressive model

Diebold-Mariano test for predictive accuracy

Double-Seasonal Holt-Winters Forecasting

Easter holidays in each season

Exponential smoothing state space model

Find dominant frequency of a time series

h-step in-sample forecasts for time series models.

forecast: Forecasting Functions for Time Series and Linear Models

Forecasting using ARIMA or ARFIMA models

Forecasting using a bagged model

Forecasting using BATS and TBATS models

Forecasting using ETS models

Forecasting using Holt-Winters objects

Forecast a linear model with possible time series components

Forecast a multiple linear model with possible time series components

Forecasting using user-defined model

Forecasting time series

Forecasting using neural network models

Forecasting using stl objects

Forecasting using Structural Time Series models

Forecasting time series

Fourier terms for modelling seasonality

Forecast plot

Time series lag ggplots

Create a seasonal subseries ggplot

Is an object constant?

Is an object a particular model type?

Is an object a particular forecast type?

Moving-average smoothing

Mean Forecast

Time Series Forecasts with a user-defined model

Compute model degrees of freedom

Multiple seasonal decomposition

Multi-Seasonal Time Series

Interpolate missing values in a time series

Naive and Random Walk Forecasts

Number of differences required for a stationary series

Neural Network Time Series Forecasts

Number of differences required for a seasonally stationary series

Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots

Plot characteristic roots from ARIMA model

Plot components from BATS model

Plot components from ETS model

Forecast plot

Multivariate forecast plot

Objects exported from other packages

Residuals for various time series models

Seasonal adjustment

Extract components from a time series decomposition

Seasonal dummy variables

Seasonal plot

Exponential smoothing forecasts

Simulation from a time series model

Forecast seasonal index

Cubic Spline Forecast

Subsetting a time series

Theta method forecast

Identify and replace outliers and missing values in a time series

Time series cross-validation

Time series display

Fit a linear model with time series components

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Maintainer: Rob Hyndman License: GPL-3 Last published: 2024-06-20

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