Automatic Time Series Forecasting with Ata Method in R: ATAforecasting Package

Ata method is a new univariate time series forecasting method that provides innovative solutions to issues faced during the initialization and optimization stages of existing methods. The Ata method's forecasting performance is superior to existing methods in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or deseasonalized time series, where the deseasonalization can be performed via any preferred decomposition method. The R package ATAforecasting was developed as a comprehensive toolkit for automatic time series forecasting. It focuses on modeling all types of time series components with any preferred Ata methods and handling seasonality patterns by utilizing some popular decomposition techniques. The ATAforecasting package allows researchers to model seasonality with STL, STLplus, TBATS, stR, and TRAMO/SEATS, and power family transformation and analyze the any time series with a simple Ata method and additive, multiplicative, damped trend the Ata methods and level fixed Ata trended methods. It offers functions for researchers and data analysts to model any type of time series data sets without requiring specialization. However, an expert user may use the functions that can model all possible time series behaviors. The package also incorporates types of model specifications and their graphs, uses different accuracy measures that surely increase the Ata method's performance

Why and how does exponential smoothing fail? An in depth comparison of ATA-simple and simple exponential smoothing

Even though exponential smoothing (ES) is publicized as one of the most successful forecasting methods in the time series literature and it is widely used in practice due to its simplicity, its accuracy can be affected by the initialization and optimization procedures followed. It also suffers from some fundamental problems that can be seen clearly when its weighting scheme is studied closely. Exponential smoothing fails to account for the number of data points that can contribute to the forecast when assigning weights to historical data. ATA smoothing has been proposed as an alternative forecasting method and is shown to perform better than ES when the accuracies are compared on empirical data. In this paper, the properties of ATA that make it stand out from ES models will be discussed by just comparing the simple versions of both models.&nbsp;Empirical performance of the two simple models will also be compared based on popular error metrics.</p

A new trend based forecasting framework ATA and its performance on the M3- competition data

Abstract—It is difficult to make predictions, especially about the future and making accurate predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. A numerous forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), and still new methods are derived or inspired from them.After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness, and accuracy as automatic forecasting procedures especially in the famous M-Competitions. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this study will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have similar structural forms and ATA can be easily adapted to all of the individual ES models however ATA has many advantages due to its innovative new weighting scheme.In this paper the focus is on modeling the trend component and handling seasonality patterns by utilizing classical decomposition. Therefore, ATA method is expanded to higher-order ES methods for additive, multiplicative, additive damped and multiplicative-damped trend components. The proposed models are called ATA trended models and their predictive performances are compared to their counter ES models on the M3 competition data set since it is still the most recent and comprehensive time-series data collection available. It is shown that the models outperform their counters on almost all settings and when a model selection is carried out amongst these trended models ATA outperforms all of the competitors in the M3- competition for both short term and long term forecasting horizons when the models’ forecasting accuracies are compared based on popular error metrics.</p

ATA Method

In this study, the forecasting accuracy of a new forecasting method that is alternative to two major forecasting approaches: exponential smoothing (ES) and ARIMA, will be evaluated. Using the results from the M3-competition, the forecasting performance of this method will be compared to not only these two major approaches but also to other successful methods derived from these two approaches with respect to simplicity and cost in addition to accuracy