hanaml.ARIMA is a R wrapper for SAP HANA PAL ARIMA algorithm.
hanaml.ARIMA(
data = NULL,
key = NULL,
endog = NULL,
exog = NULL,
order = NULL,
order.p = NULL,
order.q = NULL,
order.d = NULL,
seasonal.order = NULL,
seasonal.order.p = NULL,
seasonal.order.d = NULL,
seasonal.order.q = NULL,
seasonal.order.s = NULL,
method = NULL,
include.mean = NULL,
forecast.method = NULL,
output.fitted = NULL,
thread.ratio = NULL
)
Arguments
| data |
DataFrame
DataFrame containting the data.
|
| key |
character, optional
Name of the key. The type of key column is integer.
If not provide, defaults to the first column.
|
| endog |
character, optional
The endogenous variable, i.e. time series.
Defaults to the first non-ID column.
|
| exog |
character or list of character, optional
An optional array of exogenous variables.
Valid only for ARIMAX; cannot be the first ID column
and the name of endog column.
Defaults to NULL, i.e. no exogenous columns in data.
|
| order |
list/vector of integers, optional
(p, q, d) values of the auto regression, moving average and differentiation order.
Defaults to c(0,0,0).
|
| order.p |
integer, optional
value of the auto regression order.
Defaults to 0.
|
| order.q |
integer, optional
value of the differentiation order.
Defaults to 0.
|
| order.d |
integer, optional
Value of the differentiation order.
Defaults to 0.
|
| seasonal.order |
list of integer, optional
(P, Q, D, S) values of the auto regression, differentiation, moving average order
and seasonal period for the seasonal part.
Defaults to (0,0,0,0).
|
| seasonal.order.p |
integer, optional
value of the auto regression order for the seasonal part.
Defaults to 0.
|
| seasonal.order.d |
integer, optional
value of the differentiation order for the seasonal part.
Defaults to 0.
|
| seasonal.order.q |
integer, optional
value of the moving average order for the seasonal part.
Defaults to 0.
|
| seasonal.order.s |
integer, optional
value of the seasonal period.
Defaults to 0.
|
| method |
c("css", "mle", "css-mle"), optional
The object function for integer 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".
|
| include.mean |
logical, optional
ARIMA model includes a constant part if TRUE.
Valid only when d + D <= 1.
if d + D = 0, TRUE.
else FALSE
|
| forecast.method |
{"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". |
| output.fitted |
logical, optional
Output fitted result and residuals if TRUE.
Defaults to TRUE.
|
| thread.ratio |
double, optional
Controls the proportion of available threads that can be used by this
function.
The value range is from 0 to 1, where 0 indicates a single thread,
and 1 indicates all available threads.
Values between 0 and 1 will use up to
that percentage of available threads.Values outside this
range are ignored.
Defaults to 0.
|
Value
Returns an "ARIMA" object with the following attributes:
Details
Autoregressive Integrated Moving Average ARIMA(p, d, q) model.
Examples
Input DataFrame data:
> data$Collect()
TIMESTAMP Y
1 1 -0.63612643
2 2 3.09250865
3 3 -0.73733556
4 4 -3.14219098
5 5 2.08881981
.......
Invoke the function:
> arm <- hanaml.ARIMA(data = data,
order.p = 0,
order.d = 0,
order.q = 1,
seasonal.order.p = 1,
seasonal.order.s = 4,
method = "mle",
thread.ratio = 1.0,
output.fitted = TRUE)
Output:
> arm$fitted$Collect()
TIMESTAMP FITTED RESIDUALS
1 1 0.02337363 -0.6595001
2 2 0.11459591 2.9779127
3 3 -0.39656680 -0.3407688
4 4 0.10108234 -3.2432733
5 5 -0.43702717 2.5258470
6 6 2.34169970 0.8376030
......
See also