Forecasting of Per Capita Consumption of Electricity Based on Wavelet Decomposition

Abstract

Time series analysis is essential and takes a critical role in the areas such as finance, economics, statistic and engineering since it provides the opportunity of making decisions about the future. In time series analysis, Autoregressive (AR) models, Moving Average (MA) models, and Box-Jenkins, which includes Autoregressive Moving Average (ARMA) models, are widely used. However, in order: for the predicted results to be reliable from these models, the series must be stationary, has no noisy data and must be detrended or has no seasosanility, which is particularly problematic in financial time series. Wavelet transform (WT), which is based on the separation of time series into two components which are long-term trend and variation, has been recently used in the data preprocessing step in order to overcome that kind of problem. Thus, it provides a separate estimation model for each component of the time series and provides a more accurate estimation of its behavior. In this study, it is aimed to estimate Electricity Consumption per Capita (kWh per capita) by WT and compare the results with the traditional models. For this purpose, kWh per capita data covering the period of 1960 and 2014 of 26 different countries are used. R2, Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) criteria are used for performance comparison. According to these comparison criteria, it is observed that the performance of the prediction based on wavelet transform is better.

Keywords

• Statistic
• Time Series
• Box-Jenkins Models
• Wavelet Decomposition

Kişi Başına Elektrik Tüketiminin Dalgacık Dönüşümüne Dayalı Tahmini

Abstract

Zaman serisi analizi, finans, ekonomi, istatistik, mühendislik gibi birçok alanda gelecekle ilgili karar verme olanağı sunduğu için oldukça önemlidir. Zaman serisi analizinde otoregresif model, hareketli ortalama modeli ve otoregresif hareketli ortalama modelini kapsayan Box-Jenkins modelleri yaygın olarak kullanılmaktadır. Ancak bu modellerden elde edilen tahmin sonuçlarının güvenilir olabilmesi için, serinin durağan olması, gürültülü veri içermemesi ve trend ya da mevsimsel hareket bileşenlerinden arındırılmış olması gerekir ki özellikle finansal zaman serilerinde bu oldukça güçtür. Bu problemin üstesinden gelmek amacıyla, son zamanlarda veri önişleme adımında dalgacık dönüşümü (DD) kullanılmaktadır. DD, zaman serisinin uzun dönemli trend ve varyasyon şeklinde iki bileşene ayrılmasına dayanır. Böylece zaman serisinin her bir bileşeni için ayrı bir tahmin modeli elde edilmesi ve davranışının daha doğru bir şekilde tahmin edilmesini sağlar. Bu çalışmada Kişi Başına Elektrik Tüketiminin (KBET) DD ile tahmin edilmesi ve tahmin sonuçlarının geleneksel modeller ile karşılaştırılması amaçlanmıştır. Bu amaca yönelik olarak 26 farklı ülkenin 1960-2014 yıllarını kapsayan KBET verileri kullanılmıştır. Performans karşılaştırmaları için R2, Hata Kareler Ortalamasının Karekökü (HKOK) ve Ortalama Mutlak Yüzde Hata (OMYH) kriterleri kullanılmıştır. Bu karşılaştırma kriterlerine göre dalgacık dönüşümüne dayalı tahmin performansının daha iyi olduğu gözlenmiştir.

Keywords

• İstatistik
• Zaman Serileri
• Box Jenkins Model
• Dalgacık Dönüşüm

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