Time Series Prediction and Anomaly Detection of Light Curve Using LSTM Neural Network

Combining OC-SVMs With LSTM for Detecting Anomalies in Telemetry Data With Irregular Intervals

“…LSTM-based anomaly detection methods can be divided into two categories: classification-based methods [12,15] and error-based methods [10,[16][17][18]. Error-based methods using LSTM have great advantages over classificationbased methods in detecting anomalies where labeled sampled are barely accessible.…”

“…This method does not need any hypothesis about error distribution nor anomaly samples to determine the threshold. However, to the best of our knowledge, all the existing thresholds in LSTM-based methods are set based on merely one type of information which describe the state of the anomaly: either the error sequence [16,17], or the transformed error sequence [10,18]. Therefore, an anomaly score calculation method which considers a variety of anomaly information at the same time is defined in our method, based upon which a more accurate dynamic threshold can be found.…”

“…Anomaly detection of time series can be realized with a LSTM based classification model with the help of sliding windows [12,15], but it needs a lot of labeled anomaly data to train the supervised classification model, and it is difficult to obtain high-quality labeled anomaly data in the field of aerospace. Another more practical way is to use LSTM as a prediction model [10,[16][17][18] to get prediction sequence and the corresponding error sequence, based on which the anomalies are detected. These error-based methods show obvious advantages in domains where labels of anomalous samples are rare.…”

Pushing the technical frontier: From overwhelmingly large data sets to machine learning

“…characteristic of a single input instance but about a sequence of them. This is very helpful, among other cases, in outlier detection and hardware failure detection (Naul et al 2018;Zhang & Zou 2018).…”

PSO-Markov residual correction method based on Verhulst-Fourier prediction model

“…At present, the commonly used economic prediction models include grey prediction model (Niu et al, 2019a;Yan et al, 2019), ARIMA model (Scarpin et al, 2016;Xu et al, 2017), BP neural network model (Wang et al, 2018;Wu et al, 2016;Zhang et al, 2017), SVM regression model (Huang et al, 2019;Yin et al, 2015). However, the economic data under the time series are easily affected by external noise and show complex characteristics such as non-stationarity and randomness (Liu et al, 2017;Zhang & Zou, 2018). So, the data mining technology is usually used to establish a combined predicting model or reasonably correct the predicting residual value of the original predicting model, in order to achieve the purpose of improving the prediction accuracy.…”