Reference

Time Series Cross-Validation Functions and utilities for time series cross-validation
make_accuracy()	Estimate accuracy metrics to evaluate point forecast
make_errors()	Calculate forecast errors and percentage errors
make_future()	Convert the forecasts from a fable to a future_frame
make_split()	Create a split_frame for train and test splits per time series.
make_tsibble()	Convert tibble to tsibble
slice_train()	Slice the train data from the complete data
slice_test()	Slice the test data from the complete data
Data visualization Functions and utilities for data visualization
plot_line()	Plot data as line chart
plot_bar()	Plot data as bar chart
plot_point()	Plot data as scatterplot
plot_histogram()	Plot data as histogram
plot_density()	Plot the density via Kernel Density Estimator
plot_qq()	Quantile-Quantile plot
theme_tscv()	Custom ggplot2 theme for tscv package
theme_tscv_dark()	Dark ggplot2 theme for tscv package
scale_color_tscv()	Color scale constructor for tscv colors.
scale_fill_tscv()	Fill scale constructor for tscv colors.
tscv_cols()	Function to extract tscv colors as hex codes.
tscv_pal()	Return function to interpolate a tscv color palette.
Forecasting
DSHW The DSHW model and its supported methods
DSHW()	Automatic train a DSHW model
forecast(<DSHW>)	Forecast a trained DSHW model
fitted(<DSHW>)	Extract fitted values from a trained DSHW model
residuals(<DSHW>)	Extract residuals from a trained DSHW model
model_sum(<DSHW>)	Provide a succinct summary of a trained DSHW model
train_dshw()	Double Seasonal Holt-Winters model
ELM The ELM model and its supported methods
ELM()	Extreme Learning Machine (ELM)
forecast(<ELM>)	Forecast a trained ELM model
fitted(<ELM>)	Extract fitted values from a trained ELM model
residuals(<ELM>)	Extract residuals from a trained ELM model
model_sum(<ELM>)	Provide a succinct summary of a trained ELM model
train_elm()	Extreme Learning Machine (ELM)
MLP The MLP model and its supported methods
MLP()	Automatic training of MLPs
forecast(<MLP>)	Forecast a trained MLP model
fitted(<MLP>)	Extract fitted values from a trained MLP model
residuals(<MLP>)	Extract residuals from a trained MLP model
model_sum(<MLP>)	Provide a succinct summary of a trained MLP model
train_mlp()	Multilayer Perceptron (MLP)
SMEAN The SMEAN model and its supported methods
SMEAN()	Seasonal mean model
forecast(<SMEAN>)	Forecast a trained seasonal mean model
fitted(<SMEAN>)	Extract fitted values from a trained seasonal mean model
residuals(<SMEAN>)	Extract residuals from a trained seasonal mean model
model_sum(<SMEAN>)	Provide a succinct summary of a trained seasonal mean model
train_smean()	Seasonal mean model
SMEDIAN The SMEDIAN model and its supported methods
SMEDIAN()	Seasonal median model
forecast(<SMEDIAN>)	Forecast a trained seasonal median model
fitted(<SMEDIAN>)	Extract fitted values from a trained seasonal median model
residuals(<SMEDIAN>)	Extract residuals from a trained seasonal median model
model_sum(<SMEDIAN>)	Provide a succinct summary of a trained seasonal median model
train_smedian()	Seasonal median model
MEDIAN The MEDIAN model and its supported methods
MEDIAN()	Median model
forecast(<MEDIAN>)	Forecast a trained median model
fitted(<MEDIAN>)	Extract fitted values from a trained median model
residuals(<MEDIAN>)	Extract residuals from a trained median model
model_sum(<MEDIAN>)	Provide a succinct summary of a trained median model
train_median()	Median model
SNAIVE2 The SNAIVE2 model and its supported methods
SNAIVE2()	Seasonal naive model
forecast(<SNAIVE2>)	Forecast a trained seasonal naive model
fitted(<SNAIVE2>)	Extract fitted values from a trained seasonal naive model
residuals(<SNAIVE2>)	Extract residuals from a trained seasonal naive model
model_sum(<SNAIVE2>)	Provide a succinct summary of a trained seasonal naive model
train_snaive2()	Seasonal naive model
TBATS The TBATS model and its supported methods
TBATS()	Automatic train a TBATS model
forecast(<TBATS>)	Forecast a trained TBATS model
fitted(<TBATS>)	Extract fitted values from a trained TBATS model
residuals(<TBATS>)	Extract residuals from a trained TBATS model
model_sum(<TBATS>)	Provide a succinct summary of a trained TBATS model
train_tbats()	TBATS model
EXPERT The EXPERT model and its supported methods
EXPERT()	Automatic train a EXPERT model
forecast(<EXPERT>)	Forecast a trained EXPERT model
fitted(<EXPERT>)	Extract fitted values from a trained EXPERT model
residuals(<EXPERT>)	Extract residuals from a trained EXPERT model
model_sum(<EXPERT>)	Provide a succinct summary of a trained EXPERT model
train_expert()	EXPERT model
Miscellaneous Functions and utilities for data preparation, etc.
estimate_mode()	Estimate mode of a distribution based on Kernel Density Estimation
estimate_kurtosis()	Estimate kurtosis
estimate_skewness()	Estimate skewness
acf_vec()	Estimate the sample autocorrelation of a numeric vector
estimate_acf()	Estimate the sample autocorrelation
pacf_vec()	Estimate the sample partial autocorrelation of a numeric vector
estimate_pacf()	Estimate the sample partial autocorrelation
interpolate_missing()	Interpolate missing values
smooth_outlier()	Identify and replace outliers
lst_to_env()	Assign objects within a list to an environment
check_data()	Check, convert and shape the input data
summarise_data()	Summary statistics for time series data
summarise_stats()	Summary statistics for time series data
summarise_split()	Summary table of the splitting into training and testing
initialize_split()	Initialize a plan for train-test split
split_index()	Create indices for train and test splits.
expand_split()	Expand the split_frame
file_name()	Create a name for a folder or file
number_string()	Helper function to create numbered strings.
`%out%`	Negated value matching
log_header()	Create string with header for log file.
log_time()	Create string with elapsed time.
Data sets Example data sets
elec_load	Hourly electricity load (actual values and forecasts)
elec_price	Hourly day-ahead electricity spot prices
M4_monthly_data	Monthly time series data from the M4 Competition
M4_quarterly_data	Quarterly time series data from the M4 Competition