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