This repository contains an autoencoder for multivariate time series forecasting. It features two attention mechanisms described in A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction and was inspired by Seanny123's repository....
Our paper, therefore, proposed a new efficiency approach of anomaly detection for multivariate time series data. We specifically developed a new hybrid approach based on LSTM Autoencoder and Isolation Forest (iForest). This approach enables the advantages in extracting good features of the LSTM Auto...
Currently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is increasing. Recently, the deep architecture of th...
A multivariate time series as input to the autoencoder will result in multiple encoded vectors (one for each series) that could be concatenated. It is not clear what role averaging may take at this point, although we may guess that it is an averaging of multiple models performing the autoen...
[5] CHEVROT A, VERNOTTE A, LEGEARD B.CAE: Contextual auto-encoder for multivariate time-series anomaly detection in air transportation[J].Computers & Security, 2022, 116: 102652. [6] COELHO E SILVA L, MURÇA M C R. A data analytics framework for anomaly detection in flight operations...
To address these challenges, we introduced an attention mechanism into the LSTM-AE model and proposed an attention-based Long Short-Term Memory Autoencoder (LSTMA-AE) to model multivariate time series data from injection pumps comprehensively. Specifically, the LSTMA-AE-based anomaly detection method...
If covariates and exogenous variables are not considered, univariate time series forecasting involves predicting the future of one variable with the history of this variable, while multivariate time series forecasting involves predicting the future of C variables with the history of C variables. Note ...
Given the above, the presented study addresses said drawbacks by evaluating the effectiveness of deep-stacked S2AEs in solar irradiation and wind speed time series prediction. There is a need to test the effectiveness of deep and shallow stacked sequence-to-sequence autoencoders (S2SAE) when ...
I have read several posts like this one about training an LSTM on multivariate time series data, and have a kinda working LSTM implementation, but none of the articles have quite addressed my questions on the training strategy for my kind of data. My data is medical ECG data for a...
time stamp has a vector or array of values associated with it. Examples of multivariate time series are the (P/E, price, volume) for each time tick of a single stock or the tuple of information for each netflow between a single session (e.g. source and destination ip and port, ...