{'fraction_non_alphanumeric': 0.02338530066815145, 'fraction_numerical': 0.0, 'mean_word_length': 5.286713286713287, '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 Trend based Forecasting Model Using Fuzzy Time Series and PSO Algorithm

Time series prediction methods are vital in many areas, such as science, meteorology, agriculture, stock indices, economics, etc. To improve the forecasting accuracy, fuzzy approaches and evolutionary methods are widely used in time series forecasting models in recent years. Therefore, a hybrid method based on fuzzy time series and particle swarm optimization algorithm is proposed to establish a forecasting model. This study considers the scenario using the trend-based weighted strategy in hybrid fuzzy PSO model (THFPSO) to estimate the unknown future values. The export orders index (EOI) data of Taiwan are selected as the experimental dataset. The experimental results show that the proposed model can reduce the mean square error (RMSE) and improve accuracy comparing with other models based on fuzzy time series.