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Found 8 papers, 4 papers with code
Discovering Utility-driven Interval Rules
For artificial intelligence, high-utility sequential rule mining (HUSRM) is a knowledge discovery method that can reveal the associations between events in the sequences.
HUSP-SP: Faster Utility Mining on Sequence Data
High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity.
Totally-ordered Sequential Rules for Utility Maximization
TotalSR creates a utility table that can efficiently calculate antecedent support and a utility prefix sum list that can compute the remaining utility in O(1) time for a sequence.
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection
To minimize the reconstruction error of normal data and maximize this of anomaly data, we do not just ensure normal data to reconstruct well, but also try to make the reconstruction of anomaly data consistent with the distribution of normal data, then anomalies will get higher reconstruction errors.
VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection
In view of reconstruct ability of the model and the calculation of anomaly score, this paper proposes a time series anomaly detection method based on Variational AutoEncoder model(VAE) with re-Encoder and Latent Constraint network(VELC).
Anomaly Subsequence Detection with Dynamic Local Density for Time Series
Anomaly subsequence detection is to detect inconsistent data, which always contains important information, among time series.
An Improvement of PAA on Trend-Based Approximation for Time Series
In this paper, we propose two new approaches for time series that utilize approximate trend feature information.
Deep Convolutional Neural Networks for Thermal Infrared Object Tracking
We observe that the features from the fully-connected layer are not suitable for thermal infrared tracking due to the lack of spatial information of the target, while the features from the convolution layers are.
