四、DILATE 文章:Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models 贡献:本文在Soft-DTW算法的基础上,实现了对非平稳信号多步预测问题的处理,使得在面对发生区域剧变以及无法依赖过去信息进行推理的信号时,在兼顾了信号形状吻合的同时,显著降低了时延所带来的损失。 DILATE 算法简介 对...
时间序列预测问题是在给定过去的 length-I 序列的情况下预测未来最可能的 length-O 序列,表示为 input-I-predict-O 。long-term forecasting 设置是预测长期的未来,即 larger O。如前所述,我们强调了长期序列预测的困难:处理复杂的时间模式,打破计算效率和信息利用的瓶颈。 为了解决这两个挑战,我们将分解作为内置...
LSTF（Long Sequence Time-Series Forecasting）问题是指在时间序列预测中需要处理长序列的情况。在实际应...
LSTF（Long Sequence Time-Series Forecasting）问题是指在时间序列预测中需要处理长序列的情况。在实际应...
Deep Learning for Time Series ForecastingPredict the Future with MLPs, CNNs and LSTMs in PythonJason Brownlee
Deep Learning for Time Series Forecasting - Predict the Future with MLPs, CNNs and LSTMs in Python 下载积分:1595 内容提示: Deep Learning for Time Series ForecastingPredict the Future with MLPs, CNNs and LSTMs in PythonJason Brownlee
型轮机模拟器中的船舶辅锅炉仿真模块进行试验研采集的故障数据,试验在Pycharm平台与Python3.7 究。选取辅锅炉燃烧系统典型的4类故障开展故障环境下依托Keras以及Tensoflow深度学习框架下完 预测研究,即燃油预热器脏堵、燃油供给泵磨损、燃成编程计算。 油供给泵故障和牵引杆点火器故障。2.21DCNN处理时间序列数据 ...
Updated Jan 24, 2023 Python Spandan2308 / Sea-Ice-Extent-forecasting-using-hybrid-LSTM Star 0 Code Issues Pull requests The Sea Ice Extent of 5 Arctic and Antarctic regions is forecasted using CNN+LSTM, Bidirectional LSTM and Standalone LSTM. lstm bidirectional-lstm arctic-sea-ice time-...
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai python machine-learning timeseries deep-learning time-series regression cnn pytorch rocket transformer forecasting classification rnn seque...
后来的一些方法,例如Deep State Space Models for Time Series Forecasting(2018)等,也是基于这种概率估计的思路,模型拟合均值和方差,进而得到时间序列的概率分布。在预测结果的时候,模型同样预测的是高斯分布的均值和方差,利用每个时间步的高斯分布预测结果,我们就可以得到时间序列的一个预测区间范围,例如取某个分位数,...