Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A neural network approach for the Theta model
AU - Constantinidou, Christina
AU - Nikolopoulos, Konstantinos
AU - Bougioukos, Nikolaos
AU - Tsiafa, Eirini
AU - Petropoulos, Fotios
AU - Assimakopoulos, Vassilios
PY - 2012
Y1 - 2012
N2 - Classic Theta model decomposes the original data series into two separate lines, which are extrapolated separately and the forecasts are combined with equal weights. The current study explores a neural network approach to Theta model, in terms of optimizing the combination weights of the two components in the final forecast. The performance of the proposed method (Theta AI) is compared against the original method for the two subsets of the NN3 forecasting competition, which primary objective was the evaluation of methods using neural networks or artificial intelligence for time series forecasting. Results indicate that the new approach is very promising towards the generalization of the Theta model.
AB - Classic Theta model decomposes the original data series into two separate lines, which are extrapolated separately and the forecasts are combined with equal weights. The current study explores a neural network approach to Theta model, in terms of optimizing the combination weights of the two components in the final forecast. The performance of the proposed method (Theta AI) is compared against the original method for the two subsets of the NN3 forecasting competition, which primary objective was the evaluation of methods using neural networks or artificial intelligence for time series forecasting. Results indicate that the new approach is very promising towards the generalization of the Theta model.
KW - Time Series Forecasting
KW - Theta Model
KW - Neural Networks
KW - Artificial Intelligence
KW - Forecasting Competitions
KW - Theta AI
M3 - Journal article
VL - 25
SP - 116
EP - 120
JO - Lecture Notes in Information Technology
JF - Lecture Notes in Information Technology
SN - 2070-1918
ER -