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