使用希尔伯特·黄变换法,用搜索引擎的声音还原游客前往印尼的预测

H. Mukhtar, Yoze Rizki, Febby Apri Wenando, Muhammad Abdul Al Aziz
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引用次数: 0

摘要

在许多研究中,谷歌趋势数据是有效的分析和估计解释变量,包括旅游预测。然而,数据检索和旅游一直受到噪声的困扰。如果没有噪声处理,搜索引擎数据的预测能力可能会很弱,甚至无效。希尔伯特-黄变换(Hilbert-Huang Transform, HHT)作为一种噪声处理方法,可以降低或清除噪声。预测是预测未来事件的艺术和科学。LSTM能够克服长期依赖。本研究尝试利用Hilbert-Huang变换方法,对搜索引擎中的噪音进行处理,以提供对游客访问量的预测。所构建的预测架构由3个隐藏的LSTM层组成,其中包含100个神经元或神经单位,这些神经元或神经单位用于处理信息,在LSTM层中也成为输入层。在156行数据集上的预测测试结果,得出2019年的RMSE值,得到RMSE LSTM 129249结果,RMSE HHT + LSTM 653058。使结果RMSE更接近于记住0。
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Prediksi Kunjungan Wisatawan Ke Indonesia Dengan Reduksi Noise Pada Mesin Pencari Menggunakan Metode Hilbert Huang Transform
  In many studies, Google Trends Data is efficient to analyze and estimate as explanatory variables, including tourism predictions. However, data retrieval and tourism are always plagued by noise. Without noise processing, the predictive ability of search engine data may be weak, even invalid. As a noise processing method, Hilbert-Huang Transform (HHT) can reduce or clean noise. Forecasting is the art and science of predicting future events. LSTM is able to overcome long-term dependence. This study tries to provide predictions of tourist visits by processing noise in search engines using the Hilbert-Huang Transform method. The forecasting architecture that is built is composed of 3 hidden LSTM layers with 100 units of neurons or nerves that function to process information, which in the LSTM layer also becomes the input layer. Prediction test results on a dataset of 156 rows, resulting in RMSE values in 2019 getting RMSE LSTM 129249 results, and RMSE HHT + LSTM 653058. so that the resulting RMSE is closer to remembering 0.
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