基于贝叶斯优化的多步单变量时间序列预测自动滞后选择：孟加拉国9个分区城市的站点月度降雨量预测
Automated lag-selection for multi-step univariate time series forecast using Bayesian Optimization: Forecast station-wise monthly rainfall of nine divisional cities of Bangladesh
AI总结：
针对LSTM用于单变量时间序列降雨预测时的滞后值选择难题，本研究提出用贝叶斯优化自动调优LSTM滞后值及结构超参数的方法，实现孟加拉国9个分区城市的月度降雨量预测，模型性能优于多种对比方法。
摘要：
降雨是核心水文要素，孟加拉国这类农业国的多数经济活动都依赖降雨，精准的降雨预测可辅助制定必要决策，减少强降雨或少雨至无雨情况造成的损失。月平均降雨量属于时间序列数据，近期长短期记忆网络（LSTM）被大量用于时间序列预测任务，使用LSTM开展预测的一大核心挑战是选择合适的滞后值数量。本研究将滞后值数量设为LSTM的超参数，将其与决定LSTM结构的其他超参数一同通过贝叶斯优化完成调优。我们利用所提方法对孟加拉国9个不同气象站点的降雨量开展预测，最终将所提模型的性能，与采用其他不同滞后选择方法的LSTM模型、以及若干主流机器学习预测模型和统计预测模型进行了对比。
英文摘要：
Rainfall is an essential hydrological component, and most of the economic activities of an agrarian country like Bangladesh depend on rainfall. An accurate rainfall forecast can help make necessary decisions and reduce the damages caused by heavy or low to no rainfall. The monthly average rainfall is a time series data, and recently, long short-term memory (LSTM) neural networks are being used heavily for time series forecasting problems. One major challenge of forecasting using LSTMs is to select the appropriate number of lag values. In this research, we considered the number of lag values selected as a hyperparameter of LSTM; it, with the other hyperparameters determining LSTMs structure, has been optimized using Bayesian optimization. We used our proposed method to forecast rainfall for nine different weather stations of Bangladesh. Finally, the performance of the proposed model has been compared with some other LSTM with different lag-selection methods and some several popular machine learning and statistical forecasting models.
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。
