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