Energy Associated Tuning Method for Short-Term Series Forecasting by Complete and Incomplete Datasets

Abstract

This article presents short-term predictions using neural networks tuned by energy associated to series based-predictor filter for complete and incomplete datasets. A benchmark of high roughness time series from Mackay Glass (MG), Logistic (LOG), Henon (HEN) and some univariate series chosen from NN3 Forecasting Competition are used. An average smoothing technique is assumed to complete the data missing in the dataset. The Hurst parameter estimated through wavelets is used to estimate the roughness of the real and forecasted series. The validation and horizon of the time series is presented by the 15 values ahead. The performance of the proposed filter shows that even a short dataset is incomplete, besides a linear smoothing technique employed; the prediction is almost fair by means of SMAPE index. Although the major result shows that the predictor system based on energy associated to series has an optimal performance from several chaotic time series, in particular, this method among other provides a good estimation when the short-term series are taken from one point observations.

Short and Long-Term Time Series Forecasting Stochastic Analysis for Slow Dynamic Processes

“…In this paper, a classical non-linear autoregressive model based on neural networks whose parameters are batch tuned [11] and its performance is evaluated by stochastic analysis is proposed [12].…”

“…Several experiences had been obtained from previous works detailed in [12]. Here, an NN-based AR filter model is tuned.…”

“…The NN used is a time lagged feed-forward networks class. The NN topology consists of l x inputs, one hidden layer of H o neurons that are the processing nodes in the hidden layer, and one output neuron as shown [12]. The learning rule used in the learning process is based on the Levenberg-Marquardt method [14].…”

Multi-layer neural networks for sales forecasting

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Waste flows management by their prediction in a production company

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