Package index • tscv

Time Series Cross-Validation

Functions and utilities for time series cross-validation

make_split(): Create train-test splits for time series cross-validation
slice_train(): Slice training data from a split frame
slice_test(): Slice test data from a split frame
split_index(): Create indices for train and test splits
make_future(): Convert forecasts to a future frame
make_tsibble(): Convert data to a tsibble

Forecast Accuracy

Functions and utilities for forecast accuracy

make_accuracy(): Estimate point forecast accuracy
make_errors(): Calculate forecast errors and percentage errors
me_vec(): Calculate the mean error
mae_vec(): Calculate the mean absolute error
mse_vec(): Calculate the mean squared error
rmse_vec(): Calculate the root mean squared error
mpe_vec(): Calculate the mean percentage error
mape_vec(): Calculate the mean absolute percentage error
smape_vec(): Calculate the symmetric mean absolute percentage error

Data Analysis

Functions and utilities for data analysis, etc.

estimate_mode(): Estimate the mode of a distribution
estimate_kurtosis(): Estimate kurtosis
estimate_skewness(): Estimate skewness
acf_vec(): Estimate autocorrelations of a numeric vector
estimate_acf(): Estimate autocorrelations by time series
pacf_vec(): Estimate partial autocorrelations of a numeric vector
estimate_pacf(): Estimate partial autocorrelations by time series
interpolate_missing(): Interpolate missing values
smooth_outlier(): Identify and replace outliers
check_data(): Check and prepare tsibble data
summarise_data(): Summarise time series data
summarise_stats(): Summarise distributional statistics by time series
summarise_split(): Summarise train-test splits

Data Visualization

Functions and utilities for data visualization

plot_bar(): Plot data as a bar chart
plot_density(): Plot a kernel density estimate
plot_histogram(): Plot data as a histogram
plot_line(): Plot data as a line chart
plot_point(): Plot data as a scatterplot
plot_qq(): Create a quantile-quantile plot
theme_tscv(): Custom ggplot2 theme for tscv
scale_color_tscv(): Create a tscv color scale
scale_fill_tscv(): Create a tscv fill scale
tscv_cols(): Extract tscv colors
tscv_pal(): Create a tscv color palette

Forecasting

DSHW

The DSHW model and its supported methods

DSHW(): Double Seasonal Holt-Winters model
forecast(<DSHW>): Forecast a DSHW model
fitted(<DSHW>): Extract fitted values from a DSHW model
residuals(<DSHW>): Extract residuals from a DSHW model
model_sum(<DSHW>): Summarize a DSHW model

SMEAN

The SMEAN model and its supported methods

SMEAN(): Seasonal mean model
forecast(<SMEAN>): Forecast a seasonal mean model
fitted(<SMEAN>): Extract fitted values from a seasonal mean model
residuals(<SMEAN>): Extract residuals from a seasonal mean model
model_sum(<SMEAN>): Summarize a seasonal mean model

SMEDIAN

The SMEDIAN model and its supported methods

SMEDIAN(): Seasonal median model
forecast(<SMEDIAN>): Forecast a seasonal median model
fitted(<SMEDIAN>): Extract fitted values from a seasonal median model
residuals(<SMEDIAN>): Extract residuals from a seasonal median model
model_sum(<SMEDIAN>): Summarize a seasonal median model

MEDIAN

The MEDIAN model and its supported methods

MEDIAN(): Median model
forecast(<MEDIAN>): Forecast a median model
fitted(<MEDIAN>): Extract fitted values from a median model
residuals(<MEDIAN>): Extract residuals from a median model
model_sum(<MEDIAN>): Summarize a median model

SNAIVE2

The SNAIVE2 model and its supported methods

SNAIVE2(): Seasonal naive model with weekday-specific lags
forecast(<SNAIVE2>): Forecast a SNAIVE2 model
fitted(<SNAIVE2>): Extract fitted values from a SNAIVE2 model
residuals(<SNAIVE2>): Extract residuals from a SNAIVE2 model
model_sum(<SNAIVE2>): Summarize a SNAIVE2 model

TBATS

The TBATS model and its supported methods

TBATS(): TBATS model
forecast(<TBATS>): Forecast a TBATS model
fitted(<TBATS>): Extract fitted values from a TBATS model
residuals(<TBATS>): Extract residuals from a TBATS model
model_sum(<TBATS>): Summarize a TBATS model

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