(Partial) Autocorrelation and Cross-Correlation Function Estimation
Fit ARIMA model to univariate time series
Automatic selection of Box Cox transformation parameter
k-fold Cross-Validation applied to an autoregressive model
Fit a fractionally differenced ARFIMA model
Errors from a regression model with ARIMA errors
Return the order of an ARIMA or ARFIMA model
Fit best ARIMA model to univariate time series
ggplot (Partial) Autocorrelation and Cross-Correlation Function Estimation and Plotting
Plot time series decomposition components using ggplot
Automatically create a ggplot for time series objects
BATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
Box-Cox and Loess-based decomposition bootstrap.
Check that residuals from a time series model look like white noise
Diebold-Mariano test for predictive accuracy
Find dominant frequency of a time series
h-step in-sample forecasts for time series models.
forecast: Forecasting Functions for Time Series and Linear Models
Forecasting using ARIMA or ARFIMA models
Forecasting using Structural Time Series models
Forecasts for intermittent demand using Croston's method
Forecast a linear model with possible time series components
Forecast a multiple linear model with possible time series components
Returns local linear forecasts and prediction intervals using cubic smoothing splines estimated with 'spline_model()'.
Get response variable from time series model.
Histogram with optional normal and kernel density functions
Is an object a particular forecast type?
Time Series Forecasts with a user-defined model
Interpolate missing values in a time series
Number of differences required for a stationary series
Number of differences required for a seasonally stationary series
Osborn, Chui, Smith, and Birchenhall Test for Seasonal Unit Roots
Plot characteristic roots from ARIMA model
Residuals for various time series models
Extract components from a time series decomposition
Forecasting model using STL with a generative time series model
TBATS model (Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components)
Identify and replace outliers and missing values in a time series
Fit a linear model with time series components
Identify and replace outliers in a time series
Quarterly production of woollen yarn in Australia