RiskTSSeasonality

Description

RiskTSSeasonality(seasonality type, seasonal period, {seasonal terms}, starting index) specifies that a time series function will have the specified seasonality applied to the process result.

The seasonality type specifies the seasonality method, seasonal period is the seasonal period size, {seasonal terms} is the seasonality definition string, and starting index is the index within the seasonal period where the seasonalization begins.

Examples

RiskGBM(0.2, 0,05, RiskTSSeasonality(3,4,{0.1, 0.3, 0.5, 0.2}, 1)) will apply additive seasonality to the time series process, using 4 periods, beginning with period 1.

Guidelines

The RiskTSSeasonality property function is automatically added to fitted time series functions if the original data was transformed using Deseasonalization.

The seasonality type is specified as a value between 0 and 3, where. 0=None, 1= First order seasonal differencing, 2=Second order seasonal differencing, and 3=Additive seasonality.

The seasonal period is the number of seasonal periods. For example, it is 4 for quarterly data, 12 for monthly data, or 24 for hourly data.

The {seasonal terms}, is the seasonality definition. For  seasonal integration, this provides the integration constants, and for additive seasonality it provides the additive terms.

The starting index is the starting index in the seasonal period for additive seasonality. For example, the time series for a process with monthly seasonal data would have a starting index of 5 if the process began in May instead of January.