{'fraction_non_alphanumeric': 0.020380434782608696, 'fraction_numerical': 0.0, 'mean_word_length': 4.7578125, '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}}
Short-term prediction of the earthquake through Neural Networks and Meta-Learning

Although it is impossible to accurately predict an event such as an earthquake, this paper seeks to use Neural Networks to predict the earthquake in short time. However, the normal training method, such as backpropagation, do not work well for the complex time series as an earthquake. In this paper, we use the Meta-Learning and Transfer-Learning to improve the learning process for the prediction of time series. It is taken as data for simulations characteristic of an earthquake such as magnitude and inter-event time. This study has a sensible idea of what can happens and then do corresponding analysis in the research area related to the subject.