深度神经网络的广泛应用也归功于强大的开源框架,比如TensorFlow和PyTorch。它们利用自动微分计算反向传播的梯度,无需提前显式导出梯度。这种灵活性使得深度神经网络能够超越传统的分类和回归模型。 许多论文已经研究了机器学习在金融时间序列预测中的应用,它们通常将潜在的预测问题构建为标准的回归或分类任务(Gu等,2017;Kim...
time-seriespytorchforecastingautoencodermultivariate-timeseriesattention-mechanismslstm-autoencoder UpdatedDec 24, 2024 Python chibui191/bitcoin_volatility_forecasting Star230 GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging...
一是不同传感器之间有着非常不同的行为,即图中节点的数值和分布差异很大,因此需要考虑如何对传感器,即图中节点进行特征表示;二是GNNs的输入必须是整个图,即包括图中节点的特征表示以及各节点的连接关系,而在本文场景中,各节点之间的关系都是未知的(以往的方法是直接认为各节点之间都存在关系,即使用完全图表征各节点...
Variational Autoencoder for Dimensionality Reduction of Time-Series Topics python deep-learning time-series molecular-dynamics computational-biology pytorch computational-chemistry variational-autoencoder Resources Readme License MIT license Activity Custom properties Stars 186 stars Watchers 13 watching...
The autoencoder is used to reconstruct the input time series data, and the GAN structure is used to constrain the output of the encoder and the reconstructed output of the autoencoder to make the autoencoder more stable in the training process. Although the above methods achieved remarkable ...
Variational auto-encoder (VAE) VAE [5,29] is also a semi-supervised reconstruction-based method for anomaly detection. Its main component is a deep generative Bayesian network, a.k.a. probabilistic encoders and decoders, with the latent variablezand the observed variablex. The idea of VAE ...
Autoencoding Gaussian Mixture Model (DAGMM) [65] is a deep learning method for anomaly detection based on reconstruction, which assumes that anomalies cannot be effectively reconstructed from low-dimensional projections. DAGMM utilizes the autoencoder to reconstruct the input data and the Gaussian ...
Deep learning Visual analytics Time series Masked AutoEncoder 1. Introduction Deep learning is the field of Artificial Intelligence that studies the creation of learning systems through the use of artificial neural networks. In the last few years, this field has experienced greater growth than any ot...
Autoformer: Decomposition transformers with auto-correlation for long-term series forecastingCodeNeurIPS 2021 Whittle Networks: A Deep Likelihood Model for Time SeriesCodeICML 2021 Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series ForecastingCodeICML 2021 ...
Multivariable ETT Electricity Exchange Traffic Weather ILI Autoformer Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting Pytorch NeurIPS 2021 Multivariable PeMSD4 PeMSD8 Traffic ADI M4 ,etc Error Adjusting for Autocorrelated Errors in Neural Networks for Time Series...