build_single_model: Forecasting model for 'dockless_df' object

In luukvdmeer/dockless: Spatio-Temporal Forecasts for Bike Availability in Dockless Bike Sharing Systems

Description

Fits a forecasting model on a dockless_df time series. If the time series is non-seasonal, an ARIMA model will be fitted on the log transformed data. If the time series is seasonal, the log transformed data will be decomposed with STL decomposition, and an ARIMA model will be fitted on the seasonally adjusted data.

Usage

build_single_model(data, auto_seasonality = TRUE, seasons = list(NULL,
  96, 672, c(96, 672)))

Arguments

data	object of class dockless_df.
auto_seasonality	logical. If TRUE, the seasonal periods of the time series will be automatically determined with time series cross-validation.
seasons	if auto_seasonality is set to TRUE: a list of different seasonal period lengths - or, in the case of multiple seasonality, combinations of seasonal periods lenghts - in time lags to be tested for in the time series cross-validation. No seasonality should be represented by NULL. If auto_seasonality is set to FALSE: the seasonal period length - or, in the case of multiple seasonality, combination of seasonal period lenghts - in time lags. No seasonality should be represented by NULL.

Value

Returns an object of class ARIMA for non-seasonal data and an object of class stlm for seasonal data. Both classes come from the forecast package.

luukvdmeer/dockless documentation built on May 10, 2019, 1:24 p.m.