使用Google趋势预测流感流行率的关注序列到序列
Sequence to Sequence with Attention for Influenza Prevalence Prediction using Google Trends
摘要：
对流感流行的早期预测可减少其影响。已经进行了各种研究来预测流感感染者的数量。然而，这些研究不是高度准确的，特别是在遥远的未来，如超过一个月。为了解决这个问题，我们使用Google趋势数据研究了具有注意力的序列到序列（Seq2Seq）模型，以评估和预测多周内流感感染者的数量。Google趋势数据有助于补偿包括统计数据在内的黑暗数字，并提高预测准确性。我们证明，注意力机制是非常有效的，以提高预测精度，并实现了最先进的结果，与皮尔逊相关系数和均方根误差分别为0.996和0.67。但对流感流行高峰的预测精度还不够，需要进一步研究来克服这一问题。
英文摘要：
Early prediction of the prevalence of influenza reduces its impact. Various studies have been conducted to predict the number of influenza-infected people. However, these studies are not highly accurate especially in the distant future such as over one month. To deal with this problem, we investigate the sequence to sequence (Seq2Seq) with attention model using Google Trends data to assess and predict the number of influenza-infected people over the course of multiple weeks. Google Trends data help to compensate the dark figures including the statistics and improve the prediction accuracy. We demonstrate that the attention mechanism is highly effective to improve prediction accuracy and achieves state-of-the art results, with a Pearson correlation and root-mean-square error of 0.996 and 0.67, respectively. However, the prediction accuracy of the peak of influenza epidemic is not sufficient, and further investigation is needed to overcome this problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
