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Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions
We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks.
Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs
One of the objectives of such demand response management is to incentivize the consumers to adjust their consumption so that the overall electricity procurement in the wholesale markets is minimized, e. g. it is desirable that consumers consume less during peak hours when cost of procurement for brokers from wholesale markets are high.
Handling Variable-Dimensional Time Series with Graph Neural Networks
Such a combinatorial generalization is achieved by conditioning the layers of a core neural network-based time series model with a "conditioning vector" that carries information of the available combination of sensors for each time series.
Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation
Deep learning models such as those based on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) fail to explicitly leverage this potentially rich source of domain-knowledge into the learning procedure.
Meta-Learning for Few-Shot Time Series Classification
We overcome this limitation in order to train a common agent across domains with each domain having different number of target classes, we utilize a triplet-loss based learning procedure that does not require any constraints to be enforced on the number of classes for the few-shot TSC tasks.
Meta-Learning for Black-box Optimization
Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free black-box function optimization.
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification
We also provide qualitative insights into the working of CTN by: i) analyzing the activations and filters of first convolution layer suggesting the filters in CTN are generically useful, ii) analyzing the impact of the design decision to incorporate multiple length decisions, and iii) finding regions of time series that affect the final classification decision via occlusion sensitivity analysis.
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks
We, therefore, conclude that pre-trained deep models like TimeNet and HealthNet allow leveraging the advantages of deep learning for clinical time series analysis tasks, while also minimize dependence on hand-crafted features, deal robustly with scarce labeled training data scenarios without overfitting, as well as reduce dependence on expertise and resources required to train deep networks from scratch.
