Kısa dönemli tahminlerde kullanılan uyarlamalı üstel düzleştirme için bulanık ayarlama yaklaşımı

Yıl 2017, Cilt: 23 Sayı: 1, 88 - 94, 01.03.2017

Yunus Biçen

Öz

Uyarlamalı
düzleştirme metotları zaman serilerinin karakteristik değişimleri üzerindeki
tahmin sonuçlarını iyileştirmek için önerilmişlerdir. Zaman içerisinde var olan
uyarlamalı düzleştirme metotları çeşitlenmiştir. Birçoğu Trigg & Leach
olarak isimlendirilen orijinal basit metottan çok farklı olup, doğruluğu
artırmak için karmaşık mantıksal veya matematiksel önermeler içermektedir. Bu
makalede Bulanık Ayarlamalı Üstel Düzleştirme olarak isimlendirilen yeni bir
metot sunulmaktadır. Bu metot özellikle seviye kayması veya seviye kaymasıyla
beraber aykırı sapmaların bulunduğu zaman serileri için tahmin doğruluğunun
iyileştirilmesinde başarılıdır. Ampirik uygulama ‘The M2-Competition Time Series’
üzerinde gerçekleştirilmiştir. İstatistiksel analiz sonuçları tahmin doğruluğu
açısından bu metodun klasik uyarlamalı üstel düzleştirme metodunu geride
bıraktığını göstermektedir. Buna ek olarak önerilen metot diğer gelişmiş
uyarlanabilir metotlarla karşılaştırıldığında oldukça basittir.

Anahtar Kelimeler

Uyarlamalı üstel düzleştirme, Sapma, Tahmin, Bulanık matık, Seviye kayması, Zaman serileri

Fuzzy tuning approach for adaptive exponential smoothing used in short-term forecasts

Öz

Adaptive
smoothing methods were suggested to improve forecast results on the
characteristic changes of time series. The existing adaptive smoothing methods
have been diversified over the years. Many of them are comprised of complicated
logical or mathematical propositions for improving forecast accuracy, which are
very different from the original simple method called Trigg and Leach method. A
new method named Fuzzy Tuning Exponential Smoothing is introduced in this paper
introduces. This method is successful in improving the forecast accuracy,
especially for the time series including level shift or level shift with
outlier deflection. The empirical application carried out on ‘The
M2-Competition Time Series’. The statistical analysis results demonstrate that
the method outperforms classical adaptive smoothing method in terms of
forecasting accuracy. In addition, the proposed method is relatively simple
compared to other advanced adaptive methods.

Anahtar Kelimeler

Adaptive exponential smoothing, Deflection, Forecasting, Fuzzy logic, Level shift, Time series

Kaynakça

• Weron R. "Electricity price forecasting: A review of the state-of-the-art with a look into the future". International Journal of Forecasting, 30(4), 1030-1081, 2014.
• Göb R, Kristina L, Antonio P. "Electrical load forecasting by exponential smoothing with covariates." Applied Stochastic Models in Business and Industry, 29(6), 629-645 2013.
• Dahiya RC, Alan JG. "Adaptive exponential smoothing models for reliability estimation". IEEE Transactions on Reliability, 23(5), 332-334, 1974.
• Macaira PM, et.al. "Forecasting Brazil's electricity consumption with pegels exponential smoothing techniques". IEEE Latin America Transactions, 14(3), 1252-1258, 2016.
• Christodoulos C, Christos M, Dimitris V. "On the combination of exponential smoothing and diffusion forecasts: An application to broadband diffusion in the OECD area". Technological Forecasting and Social Change, 78(1), 163-170, 2011.
• Trigg DW, Leach AG. “Exponential smoothing with an adaptive response rate”. Operational Research Quarterly, 18(1), 53-59 (1967).
• Williams DW, Miller D. “Level-adjusted exponential smoothing for modeling planned discontinuities”. International Journal of Forecasting, 15(3), 273-289, 1999
• Monfared MAS, Ghandali, R, Esmaeili M. “A new adaptive exponential smoothing method for non-stationary time series with level shifts”. Journal of Industrial Engineering International, 10(4), 209-216, 2014.
• Taylor JW. “Smooth transition exponential smoothing”. Journal of Forecasting, 23(6), 385-404, 2004.
• Fildes R. “Quantitative forecasting-the state of the art: extrapolative models”. Journal of the Operational Research Society, 30(8), 691-710, 1979.
• Makridakis S, et.al. “The accuracy of extrapolation (time series) methods: Results of a forecasting competition”. Journal of Fore-casting, 1(2), 111-153, 1982.
• Chow WM. “Adaptive control of the exponential smoothing constant”. Journal of Industrial Engineering, 16(5), 314-317,1965.
• Roberts SD, Reed Jr. R. “The development of a self-adaptive forecasting technique”. AIIE transactions, 1(4), 314-322, 1969.
• Mentzer JT, Gomes R. “Further extensions of adaptive extended exponential smoothing and comparison with the M-Competition”. Journal of the Academy of Marketing Science, 22(4), 372-382, 1994.
• Mentzer JT. “Forecasting with adaptive extended exponential smoothing”. Journal of the Academy of Marketing Science, 16(3), 62-70, 1988.
• Whybark DC. “Comparison of adaptive forecasting techniques”. Logisics and Transportation Review, 8, 13-26, 1973.
• Dennis JD. “A performance test of a run-based adaptive exponential smoothing”. Production and Inventory Management, 19, 43-46, 1978.
• Rao AG, Shapiro A. “Adaptive smoothing evolutionary spectra”. Management Science, 17(3), 208-281, 1970.
• Pantazopoulos SN, Pappis CP. “A new adaptive method for extrapolative forecasting algorithms”. European Journal of Operational Research, 94(1), 106-111, 1996.
• Gardner ES. “Exponential smoothing: The state of the art-Part II”. International Journal of Forecasting, 22(4), 637-666, 2006.
• Taylor JW. “Volatility forecasting with smooth transition exponential smoothing”. International Journal of Forecasting, 20(2), 273-286, 2004.
• Sudheer G, Suseelatha A. “Short term load forecasting using wavelet transform combined with holt-winters and weighted nearest neighbor models”. International Journal of Electrical Power & Energy Systems, 64, 340-346, 2015.
• Bermúdez JD, Segura JV, Vercher E. “A decision support system methodology for forecasting of time series based on soft computing”. Computational statistics & data analysis, 51(1), 177-191, 2006.
• Wang J, Zhang W, Wang J, Han T, Kong L. “A novel hybrid approach for wind speed prediction”. Information Sciences, 273, 304-318, 2014.
• Vroman P, Happiette M, Rabenasolo B. “Fuzzy Adaptation of the Holt–Winter Model for Textile Sales-forecasting”. Journal of the Textile Institute, 89(1), 78-89, 1998.
• Chan KY, Dillon TS, Singh J, Chang E. “Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg-Marquardt algorithm”. Intelligent Transportation Systems, IEEE Transactions on, 13(2), 644-654, 2012.
• Park H, James WM, David AB. "Price dynamics among US electricity spot markets". Energy Economics, 28(1), 81-101, 2006.
• Lu X, Dong ZY, Li X. "Electricity market price spike forecast with data mining techniques". Electric Power Systems Research, 73(1), 19-29, 2005.
• Mount, TD, Yumei N, Xiaobin C. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters". Energy Economics, 28(1), 62-80, 2006.
• M2-Competition Time Series Data. “International Institute of Forecasters”. https://forecasters.org (31.12.2015).