ConnectionContext
The connection to the SAP HANA system.

DataFrame
DataFrame containing the data.

character, optional
The endogenous variable, i.e. time series. cannot be the first column's name (ID column).
Defaults to the second column's name.

list of character, optional
An optional array of exogenous variables. Valid only for ARIMAX; cannot be the ID column's name and the name of endog column.
Defaults to None.

integer, optional
Value of the seasonal period.
Negative: Automatically identify seasonality by means of auto-correlation scheme.
0 or 1: Non-seasonal.
Others: Seasonal period.
Defaults to -1.

double, optional
The criterion of the auto-correlation coefficient for accepting seasonality, in the range of (0, 1). The larger it is, the less probable a time series is regarded to be seasonal.
Valid only when seasonal_period is negative.
Defaults to 0.2.

integer, optional
Order of first-differencing.
Others: Uses the specified value as the first-differencing order.
Negative: Automatically identifies first-differencing order with KPSS test.
Defaults to -1.

double, optional
The significance level for KPSS test. Supported values are 0.01, 0.025, 0.05, and 0.1. The smaller it is, the larger probable a time series is considered as first-stationary, that is, the less probable it needs first-differencing. Valid only when d is negative. Defaults to 0.05.

integer, optional
The maximum value of d when KPSS test is applied. Defaults to 2.

integer, optional
Order of seasonal-differencing. Negative: Automatically identifies seasonal-differencing order Canova-Hansen test. Others: Uses the specified value as the seasonal-differencing order. Defaults to -1.

double, optional
The significance level for Canova-Hansen test. Supported values are 0.01, 0.025, 0.05, 0.1, and 0.2. The smaller it is, the larger probable a time series is considered seasonal-stationary,that is, the less probable it needs seasonal-differencing.
Valid only when seasonal_d is negative. Defaults to 0.05.

integer, optional
The maximum value of “seasonal_d“ when Canova-Hansen test is applied.
Defaults to 1.

integer, optional
The maximum value of AR order p.
Defaults to 5.

integer, optional
The maximum value of MA order q.
Defaults to 5.

integer, optional
The maximum value of SAR order P.
Defaults to 2.

integer, optional
The maximum value of SMA order Q.
Defaults to 2.

integer, optional
The information criterion for order selection:
0: AICC
1: AIC
2: BIC.
Defaults to 0.

integer, optional
The search strategy for optimal ARMA model.
0: Exhaustive traverse
1: Stepwise traverse.
Defaults to 1.

integer, optional
The maximum value of max.p + max.q + max.seasonal.p + max.seasonal.q. Valid only when search.strategy is 0.
Defaults to 15.

integer, optional
Initial value of p. Valid only when search.strategy is 1.
Defaults to 0.

integer, optional
Initial value of q. Valid only when search.strategy is 1.
Defaults to 0.

integer, optional
Initial value of seasonal.p.
Valid only when search.strategy is 1.
Defaults to 0.

integer, optional
Initial value of seasonal.q.
Valid only when search.strategy is 1.
Defaults to 0.

integer, optional
If employing ACF/PACF to guess initial ARMA models, besides user-defined model:
0: No guess. Besides user-defined model, uses states (2, 2) (1, 1)m, (1, 0) (1, 0)m, and (0, 1) (0, 1)m meanwhile as starting states.
1: Guesses starting states taking advantage of ACF/PACF.
Valid only when search.strategy is 1.
Defaults to 1.

integer, optional
The maximum iterations for searching optimal ARMA states.
Valid only when emphsearch.strategy is 1.
Defaults to (max.p + 1) * (max.q + 1) * (max.seasonal.p + 1) * (max.seasonal.q + 1).

{"css", "mle", "css-mle"}, optional
The object function for numeric optimization.

• "css": use the conditional sum of squares.

• "mle": use the maximized likelihood estimation.

• "css-mle": use css to approximate starting values and mle to fit.

Defaults to "css-mle".

integer, optional
Controls whether to check linear model ARMA (0, 0) (0, 0)m.
0: No
1: Yes
Defaults to 1.

{"formula.forecast", "innovations.algorithm"}, optional
Store information for the subsequent forecast method.

• "formula.forecast": compute future series via formula.

• "innovations.algorithm": apply innovations algorithm to compute future series, which requires more original information to be stored

Defaults to "innovations.algorithm".

logical, optional
Output fitted result and residuals if TRUE.
Defaults to TRUE.

double, optional
Controls the proportion of available threads to use for prediction. The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates up to all available threads. Values between 0 and 1 will use that percentage of available threads.Values outside this range are ignored.
Defaults to 0.