Automatic Time Series Analysis and Forecasting using the Ata Method
Transformation Techniques for The ATAforecasting
ATAforecasting: Automatic Time Series Analysis and Forecasting using A...
Find Frequency Using Periodogram
Find Frequency Using Spectral Density Of A Time Series From AR Fit
Find Multi Frequency Using Spectral Density Of A Time Series From AR F...
Accuracy Measures for The ATAforecasting
Back Transformation Techniques for The ATAforecasting
The ATA.BoxCoxAttr function works with many different types of inputs.
Confidence Interval function for the ATA Method
The core algorithm of the ATA Method
Seasonal Decomposition for The ATAforecasting
Forecasting Method for The ATAforecasting
Specialized Plot Function of The ATAforecasting
Specialized Screen Print Function of The ATAforecasting
Automatic Time Series Analysis and Forecasting using Ata Method with B...
Attributes Set For Unit Root and Seasonality Tests
Seasonality Tests for The ATAforecasting
Lag/Lead (Shift) Function for Multivariate Series
Lag/Lead (Shift) Function for Univariate 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.
Useful links