{'fraction_non_alphanumeric': 0.015795868772782502, 'fraction_numerical': 0.0, 'mean_word_length': 5.488188976377953, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automated heuristic growing of neural networks for nonlinear time series models

In this paper, we present a method for automatically selecting the optimal architecture of feedforward neural networks to build nonlinear time series models. A heuristic method is used to do an exhaustive search of all possible input/output combinations, while adjusting the lag times and the number of nodes in a fully connected single hidden layer network. Levenberg-Marquardt optimization is performed using the stop-search method of cross-validation. Statistics are maintained for all optimized structures which permits postprocessing based on performance criteria for final model selection. The methodology is applied to a case study for developing multi-variate autoregressive models for the day-ahead forecasting of electricity prices.