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Evolutionary optimization of sparsely connected and time-lagged neural networks for time series forecasting

机译：时间序列预测的稀疏连接和时滞神经网络的进化优化

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摘要

Time series forecasting (TSF) is an important tool to support decision making (e.g., planning production resources). Artificial neural networks (ANNs) are innate candidates for TSF due to advantages such as nonlinear learning and noise tolerance. However, the search for the best model is a complex task that highly affects the forecasting performance. In this work, we propose two novel evolutionary artificial neural networks (EANNs) approaches for TSF based on an estimation distribution algorithm (EDA) search engine. The first new approach consist of sparsely connected evolutionary ANN (SEANN), which evolves more flexible ANN structures to perform multi-step ahead forecasts. The second one, consists of an automatic Time lag feature selection EANN (TEANN) approach that evolves not only ANN parameters (e.g., input and hidden nodes, training parameters) but also which set of time lags are fed into the forecasting model. Several experiments were held, using a set of six time series, from different real-world domains. Also, two error metrics (i.e., mean squared error and symmetric mean absolute percentage error) were analyzed. The two EANN approaches were compared against a base EANN (with no ANN structure or time lag optimization) and four other methods (autoregressive integrated moving average method, random forest, echo state network and support vector machine). Overall, the proposed SEANN and TEANN methods obtained the best forecasting results. Moreover, they favor simpler neural network models, thus requiring less computational effort when compared with the base EANN.
机译：时间序列预测（TSF）是支持决策的重要工具（例如，计划生产资源）。由于诸如非线性学习和噪声容忍之类的优势，人工神经网络（ANN）是TSF的先天候选者。但是，寻找最佳模型是一项复杂的任务，它会极大地影响预测性能。在这项工作中，我们基于估计分布算法（EDA）搜索引擎为TSF提出了两种新颖的进化人工神经网络（EANNs）方法。第一种新方法由稀疏连接的进化神经网络（SEANN）组成，该算法演化出更加灵活的神经网络结构以执行多步提前预测。第二种方法由自动时滞特征选择EANN（TEANN）方法组成，该方法不仅可以演化ANN参数（例如输入和隐藏节点，训练参数），而且还可以将哪组时滞输入到预测模型中。使用来自不同现实领域的六个时间序列，进行了几次实验。而且，分析了两个误差度量（即均方误差和对称平均绝对百分比误差）。将这两种EANN方法与基本EANN（无ANN结构或时滞优化）和其他四种方法（自回归综合移动平均法，随机森林，回波状态网络和支持向量机）进行了比较。总体而言，所提出的SEANN和TEANN方法获得了最佳的预测结果。此外，他们更喜欢简单的神经网络模型，因此与基本EANN相比，所需的计算工作更少。

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