hanaml.ARIMA.Rd
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,
background.size = NULL,
massive = FALSE,
group.key = NULL,
group.params = NULL
)
DataFrame
DataFrame containting the data.
character, optional
Name of the ID column.
Defaults to the first column if not provided.
character, optional
The endogenous variable, i.e. time series.
Defaults to the first non-ID column.
character or list of characters, 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
.
list/vector of integers, optional
(p, q, d) values of the auto regression, moving average and differentiation order.
If order is set, the value assignment of order.p, order.d and order.q will be ignored.
Defaults to c(0,0,0).
integer, optional
value of the auto regression order.
Defaults to 0.
integer, optional
value of the differentiation order.
Defaults to 0.
integer, optional
Value of the differentiation order.
Defaults to 0.
list of integers, optional
(P, Q, D, S) values of the auto regression, differentiation, moving average order
and seasonal period for the seasonal part.
If seasonal.order is set, the value assignment of seasonal.order.p, seasonal.order.d seasonal.order.q and seasonal.order.s will be ignored.
Defaults to (0,0,0,0).
integer, optional
value of the auto regression order for the seasonal part.
Defaults to 0.
integer, optional
value of the differentiation order for the seasonal part.
Defaults to 0.
integer, optional
value of the moving average order for the seasonal part.
Defaults to 0.
integer, optional
value of the seasonal period.
Defaults to 0.
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".
logical, optional
ARIMA model includes a constant part if TRUE.
Valid only when d + D <= 1.
if d + D = 0, TRUE.
else FALSE
{"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 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.
integer, optional
Indicates the nubmer of data points used in hanaml.
ARIMA with explainations in the predict.ARIMA function.
If you want to use the ARIMA with explainations,
you must background.size to be larger than 0 or -1(auto mode)
when initializing an hanaml.ARIMA instance the and
then set show.explainer=TRUE in the predict function.
Defaults to NULL(no explainations).
logical, optional
Specifies whether or not to use massive mode.
For parameter setting in massive mode, you could use both
group.params (please see the example below) or the original parameters.
Using original parameters will apply for all groups. However, if you define some parameters of a group,
the value of all original parameter setting will be not applicable to such group.
An example is as follows:
> ad <- hanaml.ARIMA(data=df,
massive=TRUE,
background.size=5,
group.key='ID',
group.params=list('Group_1'=list('output.fitted'=FALSE')))
In this example, as 'output_fitted' is set in group.params for Group_1, parameter setting of 'background.size' is not applicable to Group_1. Defaults to FALSE.
character, optional
The column of group key. The data type can be INT or NVARCHAR/VARCHAR.
If data type is INT, only parameters set in the group.params are valid.
This parameter is only valid when massive is TRUE.
Defaults to the first column of data if group.key is not provided.
list, optional
If the massive mode is activated (massive = TRUE),
input data shall be divided into different groups with different parameters applied.
An example is as follows:
> ad <- hanaml.ARIMA(data=df,
massive=TRUE,
background.size=5,
group.key='ID',
group.params=list("Group_1"=list("output.fitted"=FALSE)))
Valid only when massive is TRUE and defaults to NULL.
Returns a "hanaml.ARIMA" object with the following attributes:
model: DataFrame
The Fitted model.
fitted: DataFrame
Predicted dependent variable values for training data.
Set to NULL if the training data has no row IDs.
explainer: DataFrame
The with explainations with decomposition of trend, seasonal, transitory, irregular
and reason code of exogenous variables.
This attributes only returns when setting background.size in the initializing an hanaml.ARIMA instance
and show.explainer=TRUE in the predict function.
error.msg : DataFrame
Error message and only valid if massive is TRUE.
Autoregressive Integrated Moving Average ARIMA(p, d, q) model.
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
......