ATA.Shift_Mat

Lag/Lead (Shift) Function for Multivariate Series

The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>,
Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the
initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy
implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend
and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.

Ali Sabri Taylan

ATAforecasting

Automatic Time Series Analysis and Forecasting using the Ata
Method

Hanife Taylan Selamlar

Guckan Yapar

ATA.Shift_Mat function

<dl><dt>X</dt>
<dd>given matrice</dd>
<dt>direction</dt>
<dd>direction of shifting. Default is "down".</dd>
<dt>shift_by</dt>
<dd>number of rows to be shifed upwards/downwards</dd>
<dt>fill</dt>
<dd>a value to be used to fill the rows</dd></dl>

Arguments

Lag/Lead (Shift) Function for Multivariate Series — ATA.Shift_Mat

<dl>

<dt>X</dt>
<dd>given matrice</dd>


<dt>direction</dt>
<dd>direction of shifting. Default is "down".</dd>


<dt>shift_by</dt>
<dd>number of rows to be shifed upwards/downwards</dd>


<dt>fill</dt>
<dd>a value to be used to fill the rows</dd>

</dl>

ATA.Shift_Mat: Lag/Lead (Shift) Function for Multivariate Series

Description

Usage

Value

Arguments