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xLSTM; transformer; linear network; time series forecasting; state-space model1. Introduction Time series forecasting with Artificial Intelligence has been a prominent research area for many years. Historical data on electricity, traffic, finance, and weather are frequently used to train models for ...
Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal and multivariate components is crucial for reliable predictions. We introduce xLSTM-Mixer, a model designed to effectively integr...
35、本发明提出了一种基于lstm-timegan神经网络的多因素负荷预测方法,包括:采集待分析的负荷数据,经预处理后,生成格式统一的数值化数据;将所述数值化数据经归一化处理后划分训练集和测试集,使用训练集训练lstm模型和timegan模型,直到模型收敛;确定期望预测的时间点数量,使用测试集中的数据作为样本输入所述lstm模型进入...
Cross-modal Recurrent Models for Human Weight Objective Prediction from Multimodal Time-series Data - riddle-wang/xlstm
In this paper, we propose a novel variant of LSTM, named CTS-LSTM, to collectively forecast correlated time series. Specifically, spatial and temporal correlations are explicitly modeled and respectively maintained in cells to capture the complex non-linear patterns in correlated time series. A ...
Findings LSTM can predict CM prevalence with accurate trend and low mean squared error (MSE). We also found a tradeoff between the length of the forecasting period and the performance of the forecasting model. The models with longer forecasting periods have less accurate trends over time and ...
xLSTM; transformer; linear network; time series forecasting; state-space model1. Introduction Time series forecasting with Artificial Intelligence has been a prominent research area for many years. Historical data on electricity, traffic, finance, and weather are frequently used to train models for ...
The Bi-LSTM with X-11 combined model significantly improves spatial smoothness and accuracy compared to Bi-LSTM, LSTM, and ARIMA in both regions. The average RMSE values for the combined model in Greenland and the Yangtze River basin are 3.1 cm and 4.4 cm, respectively. The combined model'...
Time-series prediction of hourly atmospheric pressure using ANFIS and LSTM approachesAdaptive neuro-fuzzy inference system (ANFIS)Atmospheric pressureLong short-term memory (LSTM)Machine learning approachesOne-hour-ahead forecastingNeural Computing and Applications - Atmospheric pressure (AP), which is an ...