A NEW CONSENSUS BETWEEN THE MEAN AND MEDIAN COMBINATION METHODS TO IMPROVE FORECASTING ACCURACY

ARAS S., GÜLAY E.

SERBIAN JOURNAL OF MANAGEMENT, cilt.12, sa.2, ss.217-236, 2017 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 2
  • Basım Tarihi: 2017
  • Doi Numarası: 10.5937/sjm12-13091
  • Dergi Adı: SERBIAN JOURNAL OF MANAGEMENT
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
  • Sayfa Sayıları: ss.217-236
  • Anahtar Kelimeler: Forecasting, time series, combined forecasts, least support vector machines, neural networks, SETA.R, LSTAR, ARIMA, TIME-SERIES MODELS, NEURAL-NETWORKS, COMBINING FORECASTS, PREDICTION, AVERAGES, GDP
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

To improve the forecasting accuracies, researchers have long been using various combination techniques. In particular, the use of dissimilar methods for forecasting time series data is expected to provide superior results. Although numerous combination techniques have been proposed until datc, the simple combination techniques such as mean and median maintain their strength, popularity, and utility. This paper proposes a new combination method based on the mean and median combination methods so as to combine the advantages of both these methods. The proposed combination technique attempts to utilize the strong aspects of each method and minimize the risk that arises from the selection of the combination method with poor performance. In order to depict the potential power of the proposed combining method, well-known six real-world time series data were used. Our results indicate that the proposed method presents with promising performances. In addition, a nonparametric statistical tcst was exploited to reveal the superiority of the proposed method over the single methods and other forecast combination methods from all of the investigated data sets.