دانلود مقاله ISI انگلیسی شماره 52450
ترجمه فارسی عنوان مقاله

یک استراتژی انتخاب مدل جدید در پیش بینی سری زمانی با شبکه های عصبی مصنوعی: IHTS

عنوان انگلیسی
A new model selection strategy in time series forecasting with artificial neural networks: IHTS
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
52450 2016 14 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Neurocomputing, Volume 174, Part B, 22 January 2016, Pages 974–987

ترجمه کلمات کلیدی
شبکه های عصبی - پیش بینی - سری زمانی - انتخاب مدل
کلمات کلیدی انگلیسی
Neural networks; Forecasting; Time Series; Model Selection
پیش نمایش مقاله
پیش نمایش مقاله  یک استراتژی انتخاب مدل جدید در پیش بینی سری زمانی با شبکه های عصبی مصنوعی: IHTS

چکیده انگلیسی

Although artificial neural networks have recently gained importance in time series applications, some methodological shortcomings still continue to exist. One of these shortcomings is the selection of the final neural network model to be used to evaluate its performance in test set among many neural networks. The general way to overcome this problem is to divide data sets into training, validation, and test sets and also to select a neural network model that provides the smallest error value in the validation set. However, it is likely that the selected neural network model would be overfitting the validation data. This paper proposes a new model selection strategy (IHTS) for forecasting with neural networks. The proposed selection strategy first determines the numbers of input and hidden units, and then, selects a neural network model from various trials caused by different initial weights by considering validation and training performances of each neural network model. It is observed that the proposed selection strategy improves the performance of the neural networks statistically as compared with the classic model selection method in the simulated and real data sets. Also, it exhibits some robustness against the size of the validation data.