Accuracy measures for a forecast model
(Partial) Autocorrelation and Cross-Correlation Function Estimation
Fit a fractionally differenced ARFIMA model
Fit ARIMA model to univariate time series
Fit best ARIMA model to univariate time series
Create a ggplot layer appropriate to a particular data type
Forecasting using a bagged model
Forecasting using BATS and TBATS models
Box-Cox and Loess-based decomposition bootstrap.
Box Cox Transformation
Number of differences required for a seasonally stationary series
Automatic selection of Box Cox transformation parameter
Check that residuals from a time series model look like white noise
Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
Simulation from a time series model
Extract components of a TBATS model
Forecast seasonal index
BATS model (Exponential smoothing state space model with Box-Cox
transformation, ARMA errors, Trend and Seasonal components)
Errors from a regression model with ARIMA errors
Return the order of an ARIMA or ARFIMA model
Forecasts for intermittent demand using Croston's method
Diebold-Mariano test for predictive accuracy
Number of trading days in each season
Theta method forecast
Cross-validation statistic
Forecasting using ARIMA or ARFIMA models
Forecast a multiple linear model with possible time series components
Automatically create a ggplot for time series objects
k-fold Cross-Validation applied to an autoregressive model
Double-Seasonal Holt-Winters Forecasting
Forecasting using a bagged model
Forecasting using user-defined model
Easter holidays in each season
Forecasting using Holt-Winters objects
Time series lag ggplots
Forecasting using ETS models
Create a seasonal subseries ggplot
Time Series Forecasts with a user-defined model
Number of days in each season
Forecast a linear model with possible time series components
Forecasting time series
Daily morning gold prices
h-step in-sample forecasts for time series models.
Is an object constant?
Is an object a particular model type?
Is an object a particular forecast type?
Forecasting using neural network models
Forecasting using stl objects
Plot characteristic roots from ARIMA model
Plot components from ETS model
Forecast plot
Forecasting Functions for Time Series and Linear Models
Get response variable from time series model.
Extract components from a time series decomposition
Plot components from BATS model
Seasonal dummy variables
Histogram with optional normal and kernel density functions
Interpolate missing values in a time series
Time series display
Half-hourly electricity demand
Fit a linear model with time series components
Fourier terms for modelling seasonality
Moving-average smoothing
Naive and Random Walk Forecasts
TBATS model (Exponential smoothing state space model with Box-Cox
transformation, ARMA errors, Trend and Seasonal components)
Quarterly production of woollen yarn in Australia
Mean Forecast
Multivariate forecast plot
Objects exported from other packages
Seasonal plot
Number of differences required for a stationary series
Time series cross-validation
Exponential smoothing forecasts
Identify and replace outliers and missing values in a time series
Neural Network Time Series Forecasts
Residuals for various time series models
Seasonal adjustment
ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation
and Plotting
Plot time series decomposition components using ggplot
Exponential smoothing state space model
Find dominant frequency of a time series
Forecasting time series
Forecasting using Structural Time Series models
Australian monthly gas production
Forecast plot
Multiple seasonal decomposition
Multi-Seasonal Time Series
Cubic Spline Forecast
Subsetting a time series
Identify and replace outliers in a time series
Australian total wine sales