Utility of a time-lagged autoencoder for calculating free energy by generating a large number of synthetic trajectories based on molecular dynamicsdoi:10.1016/j.bpj.2023.11.186Soheil JamaliFauzia HaqueMahmoud MoradiElsevier Inc.Biophysical Journal
Recent work in the field of deep learning has led to the development of variational autoencoders (VAE), which are able to compress complex datasets into simpler manifolds. We present the use of a time-lagged VAE, or variational dynamics encoder (VDE), to reduce complex, nonlinear processes ...
Therefore, in this paper, we propose a Long Short-Term Memory Network based Recurrent Autoencoder with Imputation Units and Temporal Attention Imputation Model (RATAI) that imputes incomplete time series by introducing a two-stage imputation strategy of prediction and reconstruction. In the first sta...
Despite the strict dominance of the TLRN over traditional and widely known designs for prediction, for dynamic modeling, and for reconstructing the true state of the world (like autoencoders but more rigorous and powerful), there are extensions of the general TLRN which have performed much bette...
Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series Forecasting AAAI 2021 - Traffic, Electricity, Wiki introduced a novel temporal latent auto-encoder method which enables nonlinear factorization of multivariate time series, learned end-to-end with a temporal deep learning lat...
{autoencoder, generative adversarial network, boltzmann machine,\(\varnothing\)} {time series, data} {synthesis, generation,\(\varnothing\)} {evaluation, measure, metric,\(\varnothing\)}, leading to search queries such as “time series synthesis”, “data synthesis evaluation”, or “generative...
First, the autoencoder model was utilized to extract local features from the multivariate time series. Subsequently, LSTM units captured temporal information from the local features. Finally, an anomaly detection score was defined to be equal to the loss between the input and output time intervals....
Variational Auto-Encoder VAE Recent Time Series Works Grouped by Task Multivariable Time Series Forecasting Multivariable Probabilistic Time Series Forecasting Time Series Imputation Time Series Anomaly Detection Demand Prediction Time Series Generation Travel Time Estimation Traffic Location Prediction Event Predic...
p is the number of lagged observations and w represents the vector of network weights. The neural network thus functions just like a nonlinear autoregressive model. The time series data, methodologically, is first divided into two data sets, namely the training set and the testing set. For the...
automated time-series process segmentation comprises processing each of the two or more time-series data sequences using a deep learning (DL) model provided as one of a bidirectional long short-term memory (LSTM) sequence classifier using supervised learning, and an autoencoder using unsupervised ...