{'fraction_non_alphanumeric': 0.011267605633802818, 'fraction_numerical': 0.0, 'mean_word_length': 5.2368421052631575, '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}}
Hierarchical Temporal Memories Prediction Performance and Robustness to Faults on Multivariate Time Series

In this article, we evaluate the ability of Hierarchical Temporal Memories (HTM) to process values coming from sensor chains. We present a study on the impact of the HTM parameterization on its ability to predict input values and its robustness to sensor faults. The HTM is evaluated on simulated multivariate time series comprising several causal relations between variables. The results show the ability of HTM to predict future values of multivariate time series and to be robust to sensor faults. We then present which parameters most impact HTM prediction performance and its robustness to faults.