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Like the previous M competitions, M4 competition resulted in great contributions to the field of forecasting. Ata method which is a new forecasting method alternative to exponential smoothing, competed in this competition with five different models. The results obtained from these five models are discussed in detail in this paper. According to various error metrics, the models perform better than their exponential smoothing based counters. Despite their simplicity, they are ranked satisfactorily high compared to the other methods. In addition, the forecasting accuracy of simple combinations of these Ata models and ARIMA are given for the M4 competition data set. The combinations work significantly better than models that are much more complex. Therefore, besides the fact that Ata models perform well alone, Ata should be considered as a candidate for being included in combinations of forecasts.

Amaç:İdiyopatik normal basınçlı hidrosefali (iNBH), yaşlı erişkinlerde majör nörodejeneratif bozukluklarla benzer klinikbulguları paylaşan ve bu majör nörodejeneratif bozukluklara eşlik eden, bilişsel ve yürüme bozukluğunun önde gelengeri dönüşümlü bir nedenidir. Bu çalışma ile iNBH ayırıcı tanısında Alzheimer hastalığı (AH)-beyin omurilik sıvısı (BOS)biyobelirteçlerinin yararlı olup olmayacağını araştırmayı amaçladık.Gereç ve Yöntem:Amiloid-beta (Aß) 42 ve 40, toplam tau (t-tau), fosforile tau (p-tau) düzeyi, 192 ardışık BOS örneğinde (iNPH (n=80),AH ( n=48), frontotemporal demans (FTD) (n=34), Parkinson hastalığı demansı ve Lewy cisimcikli demanstan oluşanLewy cisim hastalıkları (LBH) (n=30)) ELISA ile ölçüldü.Bulgular:Çalışma popülasyonunun yaş ortalaması 75.6±7.7 yıl olup, %54.2si kadındı. iNBH hastalarında, AH ve LCH hastalarınagöre BOS Aß42 seviyeleri anlamlı olarak daha yüksekti ve p-tau ve t-tau seviyeleri daha düşüktü. Ek olarak, iNBHhastaları, FTD hastalarına göre anlamlı ölçüde daha yüksek t-tau seviyelerine sahipti. Yaş ve cinsiyete göre ayarlanmışmultinominal regresyon analizi, Aß42 seviyesi bir standart sapma (SD) arttığında iNBHye göre AH olma ihtimalinin%37 azaldığını ve LCHa sahip olma ihtimalinin de %47 azaldığını ortaya koydu. iNPHye göre LCHa sahip olmaolasılığının, p-tau seviyesi 1SD arttığında %76 artttığı gösterildi. Bir hastanın t-tau değeri 1SD arttığında NBHye göreLCH’a sahip olma olasılığı 2.5 kat, AH’ye sahip olma olasılığı 2.1 kat daha fazladır.Sonuç:Sonuçlarımız, özellikle azalmış BOS t-tau düzeyi başta olmak üzere, BOS Aβ42, p-tau ve t-tau düzeylerinin, iNBHyiLCHlardan ayırt etmede potansiyel değere sahip olduğunu göstermektedir. Ayrıca sonuçlarımız iNBHde, AHye göret-tau düzeyinin daha düşük ve Aβ42 düzeyinin daha yüksek olduğunu bildiren önceki çalışmaları doğrulamaktadır.

Like the previous M competitions, M4 competition resulted in great contributions tothe field of forecasting. Ata method which is a new forecasting method alternative toexponential smoothing, competed in this competition with five different models. Theresults obtained from these five models are discussed in detail in this paper. According tovarious error metrics, the models perform better than their exponential smoothing basedcounters. Despite their simplicity, they are ranked satisfactorily high compared to theother methods. In addition, the forecasting accuracy of simple combinations of these Atamodels and ARIMA are given for the M4 competition data set. The combinations worksignificantly better than models that are much more complex. Therefore, besides the factthat Ata models perform well alone, Ata should be considered as a candidate for beingincluded in combinations of forecasts.

Kesikli talep tahmini, şirketler ve ticarifaaliyetler için çok önemlidir. Son zamanlarda, birçok araştırmacı kesiklitalep için tahmin yöntemlerine odaklandı ve çeşitli tahmin teknikleriönerdiler. Bu önerilen teknikler arasında öne çıkan yöntemler, üsseldüzleştirmeye dayanan Croston yöntemi ve bu yöntemin iki türevi olan SBA(Syntetos-Boylan Yaklaşımı) ve SBJ (Shale-Boylan-Johnston Yaklaşımı)metotlarıdır.e Croston yöntemi, kesikli talep ve envanter (stok) kontrolününtahmininde yaygın olarak kullanılmaktadır. Bu talepler genellikle sıfırdeğerini içerdiğinden, bu verilerde Croston tarafından geliştirilen öne çıkanmetodun kullanılması kaçınılmaz hale gelir. Bununla birlikte; bu yöntemin,yanlı tahminler üretmek gibi bazı eksiklikleri vardır ve bu sebeple türevleriönerilmiştir. ATA metot, üssel düzleştirmeye alternatif olarak yenigeliştirilen bir tahmin metodudur. Bu çalışmada, kesikli talebin tahminedilmesi için bir ATA yönteminin bir modifikasyonunu öneriyoruz. Önerilen yaklaşımınsonuçlarını, Croston ve kesikli talep tahmini için kullanılan diğer tahminyöntemleriyle karşılaştıracağız., Intermittent demandforecasting is crucial for firms and commercial activities. Recently, manyresearchers have focused on forecasting methods for intermittent demand andproposed various forecasting techniques. The most prominent methods among theseproposed techniques are the Croston method, which is based on exponentialsmoothing, and its two popular variations: SBA (Syntetos-Boylan Approximation),SBJ (Shale-Boylan-Johnston Approximation). Croston method is widely used inforecasting of intermittent demand and inventory (stock) control. Since thesedemands usually include zero values, using the ground breaking method developedby Croston in this data becomes inevitable. Nevertheless, there are someshortcomings to this method such as producing biased forecasts and for thisreason its variations have been proposed. ATA method is a recently developedforecasting method which is an alternative to exponential smoothing. In thispaper we propose a modification of ATA method that can be used for forecastingof intermittent demand. We will compare the results of the proposed approach tothose of Croston and other forecasting methods used for intermittent demandforecasting.

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.

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.Empirical performance of the two simple models will also be compared based on popular error metrics.