校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average M...校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)模型。基于Chen-Liu迭代算法,研究利用R软件进行编程,成功识别了用水数据中的异常点位置、类型、异常效应的大小,以及调整后的时间序列等,由此预估管网漏损可能出现的日期和位置。研究发现,基于ARIMA时间序列模型对用水数据进行异常点的检测较为准确,且输出的异常点类型可以区分异常点是人为因素造成还是由管网漏损问题造成,进而预估管网漏损问题,这为供水行业漏损管理模式提供了一种新的方向。展开更多
金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIM...金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIMA(3,1,5)模型、LSTM模型及GRU模型虽能捕捉时间序列特征但预测存在偏差,结果表明组合模型ARIMA-LSTM预测效果优于其他三种模型。通过MAE和RMSE评估,验证了ARIMA-LSTM模型在黄金价格预测中的优势,为金融决策提供新思路。Financial time series forecasting is of great significance to economic decision-making and investment, but the complexity of financial markets brings challenges to the construction of forecasting models, and the trend of gold price has attracted much attention, so accurate forecasting is crucial. This paper aims at the shortcomings of existing combination models, an innovative nonlinear ARIMA-LSTM combined model is proposed for gold price prediction. The empirical analysis shows that although ARIMA(3,1,5) model, LSTM model and GRU model can capture the features of time series, the prediction bias exists. The results show that the combined model ARIMA-LSTM has better prediction effect than the other three models. Through MAE and RMSE evaluation, the advantages of ARIMA-LSTM model in gold price prediction are verified, which provides new ideas for financial decision-making.展开更多
文摘校园用水数据,既有趋势性又有季节性。为了准确地对智能水表收集的用水数据进行异常点分析,从而检测预估管网漏损问题,研究对用水数据进行了相关检验,并选择了合适的自回归差分移动平均模型(Autoregressive Integrated Moving Average Model,ARIMA)模型。基于Chen-Liu迭代算法,研究利用R软件进行编程,成功识别了用水数据中的异常点位置、类型、异常效应的大小,以及调整后的时间序列等,由此预估管网漏损可能出现的日期和位置。研究发现,基于ARIMA时间序列模型对用水数据进行异常点的检测较为准确,且输出的异常点类型可以区分异常点是人为因素造成还是由管网漏损问题造成,进而预估管网漏损问题,这为供水行业漏损管理模式提供了一种新的方向。
文摘金融时间序列预测对经济决策和投资意义重大,但金融市场的复杂性给预测模型构建带来挑战,而黄金价格走势备受关注,准确预测至关重要。本文针对现有组合模型不足,提出创新的非线性ARIMA-LSTM组合模型用于黄金价格预测。实证分析发现,ARIMA(3,1,5)模型、LSTM模型及GRU模型虽能捕捉时间序列特征但预测存在偏差,结果表明组合模型ARIMA-LSTM预测效果优于其他三种模型。通过MAE和RMSE评估,验证了ARIMA-LSTM模型在黄金价格预测中的优势,为金融决策提供新思路。Financial time series forecasting is of great significance to economic decision-making and investment, but the complexity of financial markets brings challenges to the construction of forecasting models, and the trend of gold price has attracted much attention, so accurate forecasting is crucial. This paper aims at the shortcomings of existing combination models, an innovative nonlinear ARIMA-LSTM combined model is proposed for gold price prediction. The empirical analysis shows that although ARIMA(3,1,5) model, LSTM model and GRU model can capture the features of time series, the prediction bias exists. The results show that the combined model ARIMA-LSTM has better prediction effect than the other three models. Through MAE and RMSE evaluation, the advantages of ARIMA-LSTM model in gold price prediction are verified, which provides new ideas for financial decision-making.
