Comparison of forecast accuracy of<b>Ata</b>and exponential smoothing

Çetin, Beyza; Yavuz, İdil

doi:10.1080/02664763.2020.1803813

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…The MAPE value is a measure that is often used in the literature. If the MAPE value is below 10%, then the model is excellent, and the range of 10% and 20% is classified as good (Cetin & Yavuz, 2021;Chai & Draxler, 2014).…”

Section: The Non-seasonal Holt-winters Methodsmentioning

confidence: 99%

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

Qur'ani,

Widyaningrum

2024

Mikailalsys J. of Math. and Statistics

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The non-seasonal Holt-Winters method is one of the methods of smoothing theory. This method can be implemented on time series data that does not have a seasonal component. In this study, this method is used to forecast the stock price returns of companies affected by the Boycott, Divestment, and Sanctions (BDS) action. Forecasting gets very good results that can be seen from the MAPE value of modeling the six stocks affiliated with Israel that continue to carry out Zionism against Palestine is not more than 10%. This method can also accommodate the limitations of existing data while still obtaining good forecasting results. In addition, the use of several transformations of stock price returns in this case is very useful in modeling to obtain appropriate error assumptions. The forecasting results of the model formed as a whole follow the trend in the stock price of each company. To produce good forecasting results using this method, it is recommended to do forecasting in the short term. The forecasting results show that of the six company stocks, almost all of them experienced a decrease in stock price returns. Only one stock of PT Map Boga Adiperkasa Tbk has increased. This also illustrates that the BDS action influences on these companies.

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Section: The Non-seasonal Holt-winters Methodsmentioning

confidence: 99%

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

Qur'ani,

Widyaningrum

2024

Mikailalsys J. of Math. and Statistics

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“…[5] compares the ATA method to Croston-based methods for forecasting intermittent demand, and [6,7] conducts a comparison between ATA and exponential smoothing methods on datasets with or without linear trends, demonstrating the superior performance of the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 İnsidans Oranlarını Modelleme ve Öngörümleme: Fransa'da Gözetim Başlangıcından Beri Akut Solunum Enfeksiyonları (ARI) Zaman Serisi Analizi

TAYLAN SELAMLAR

2024

Balıkesir Medical Journal

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Objective: This study aims to address the challenges of planning and managing the trajectory of the COVID-19 pandemic by evaluating the predictive abilities of three distinct forecasting models. The primary focus is on the ATA univariate forecasting method, ARIMA (AutoRegressive Integrated Moving Average), and ETS (Error-Trend-Seasonality) models. These models are applied to a meticulously collected dataset comprising Acute Respiratory Infections (ARI) incidence rates in France, systematically collected since the initiation of surveillance. Methods: The purpose of the study was to conduct a comprehensive evaluation of forecasting models using the selected dataset to achieve its objective. The focus was on comparing the accuracy and performance of ATA univariate forecasting, ARIMA, and ETS models in predicting COVID-19 incidence rates. Additionally, the study incorporated a combination approach proven to be effective in enhancing forecasting performance. Results: According to the results obtained regarding forecast performance, the univariate models indicate that the ATA method exhibits the highest performance, while observations reveal that combinations of ATA and ARIMA methods enhance forecast accuracy. Conclusions: In summary, the most accurate approach for forecasting future Covid-19 incidence rates, specifically those derived from Acute Respiratory Infections (ARI), has been a combination of the high-accuracy methods ATA and ARIMA. These findings enhance our understanding of the trajectory of the pandemic, providing a foundation for strategic planning and effective management.

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“…Ata method (Cetin and Yavuz, 2020;Yilmaz et al, 2019;Yapar, 2018;Yapar et al, , 2017 is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing methods. ATAforecasting performance is superior to existing methods both in terms of easy implementation and accurate forecasting.…”

Section: Introductionmentioning

confidence: 99%

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

Ali¹,

Yapar²,

Hanife³

2021

The R Journal

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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.

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Comparison of forecast accuracy ofAtaand exponential smoothing

Cited by 5 publications

References 9 publications

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

The Non-Seasonal Holt-Winters Method for Forecasting Stock Price Returns of Companies Affected by BDS Action

COVID-19 İnsidans Oranlarını Modelleme ve Öngörümleme: Fransa'da Gözetim Başlangıcından Beri Akut Solunum Enfeksiyonları (ARI) Zaman Serisi Analizi

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

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