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