{'fraction_non_alphanumeric': 0.01532033426183844, 'fraction_numerical': 0.0, 'mean_word_length': 4.9421487603305785, '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}}
A GENETIC ALGORITHM BASED DESIGN APPROACH FOR THE PROPERTIES OF A GAUSSIAN PROCESS FOR TIME SERIES FORECASTING

In this work autoregressive filters based on Gaussian processes are used to forecast time series. As a benchmark function the Mackey-Glass delay differential equation is used. The time lagged inputs of the filters and their covariance functions are determined via a multicriteria, genetic algorithm – namely the NSGA-II. The used optimization criteria are the number of inputs and the prediction error of the filters on the known data which leads to Pareto optimal solutions. The obtained filters are validated with out-of-sample data and their properties are discussed in the context of the given problem.