{'fraction_non_alphanumeric': 0.019867549668874173, 'fraction_numerical': 0.013245033112582781, 'mean_word_length': 5.146341463414634, '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}}
Application of Improved GM(1,1) Models in Seasonal Monthly Tourism Demand Forecast

The majority of tourism demand time series show patterns in terms of seasonal, cyclical and trend components, leading to low accuracy in medium and long-term data forecasting. In order to solve this problem, this paper presents an improved grey model (IGM) based on a re-shaped time series and a genetically optimized method. The monthly arrivals of tourists to Langkawi Island in Malaysia between January 2004 and December 2016 were used to verify the efficiency of the optimized model in anticipating the demand for tourism. The results show that the proposed model achieves better forecasting accuracy on the data with increasing trend, seasonal and cyclical patterns.