A Time Series is a sequence of data points, typically ordered chronologically, where each data point represents a measurement or observation recorded at a specific time or over a sequence of time intervals. Time series data is used to analyze how a variable changes over time, and it is a fundamental concept in various fields, including finance, economics, signal processing, environmental science, and more.


AutoTCL: Automated Time Series Contrastive Learning with Adaptive Augmentations

Read AutoTCL: Automated Time Series Contrastive Learning with Adaptive Augmentations publication. Modern techniques like contrastive learning have been effectively used in many areas, including computer vision, natural language processing, and graph-structured data. Creating positive examples that assist the model in learning robust and discriminative representations is a crucial stage in contrastive learning approaches. Usually, preset human intuition directs the selection of relevant data augmentations. Due to patterns that are easily recognized by humans, this rule of thumb works well in the vision and language domains. However, it is impractical to visually inspect the temporal structures in time series. The diversity of time series augmentations at both the dataset and instance levels makes it difficult to choose meaningful augmentations on the fly. Thus, although prevalent, contrastive learning with data augmentation has been less studied in the time series domain. In this study, we address this gap by analyzing time series data augmentation using information theory and summarizing the most commonly adopted augmentations in a unified format. We then propose a parameterized augmentation method, AutoTCL, which can be adaptively employed to support time series representation learning. The proposed approach is encoder-agnostic, allowing it to be seamlessly integrated with different backbone encoders. Experiments on benchmark datasets demonstrate the highly competitive results of our method, with an average 10.3% reduction in MSE and 7.0% in MAE over the leading baselines.

Ordinal Quadruplet: Retrieval of Missing Labels in Ordinal Time Series

Ordinal Quadruplet: Retrieval of Missing Labels in Ordinal Time Series In this paper, we propose an ordered time series classification framework that is robust against missing classes in the training data, i.e., during testing we can prescribe classes that are missing during training. This framework relies on two main components: (1) our newly proposed ordinal quadruplet loss, which forces the model to learn latent representation while preserving the ordinal relation among labels, (2) testing procedure, which utilizes the property of latent representation (order preservation). We conduct experiments based on real world multivariate time series data and show the significant improvement in the prediction of missing labels even with 40% of the classes are missing from training. Compared with the well known triplet loss optimization augmented with interpolation for missing information, in some cases, we nearly double the accuracy.

Deep Multi-Instance Contrastive Learning with Dual Attention for Anomaly Precursor Detection

Deep Multi-Instance Contrastive Learning with Dual Attention for Anomaly Precursor Detection Prognostics or early detection of incipient faults by leveraging the monitoring time series data in complex systems is valuable to automatic system management and predictive maintenance. However, this task is challenging. First, learning the multi-dimensional heterogeneous time series data with various anomaly types is hard. Second, the precise annotation of anomaly incipient periods is lacking. Third, the interpretable tools to diagnose the precursor symptoms are lacking. Despite some recent progresses, few of the existing approaches can jointly resolve these challenges. In this paper, we propose MCDA, a deep multi-instance contrastive learning approach with dual attention, to detect anomaly precursor. MCDA utilizes multi-instance learning to model the uncertainty of precursor period and employs recurrent neural network with tensorized hidden states to extract precursor features encoded in temporal dynamics as well as the correlations between different pairs of time series. A dual attention mechanism on both temporal aspect and time series variables is developed to pinpoint the time period and the sensors the precursor symptoms are involved in. A contrastive loss is designed to address the issue that annotated anomalies are few. To the best of our knowledge, MCDA is the first method studying the problem of ‘when’ and ‘where’ for the anomaly precursor detection simultaneously. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness of MCDA.

Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series

Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series The problem of learning and forecasting underlying trends in time series data arises in a variety of applications, such as traffic management, energy optimization, etc. In literature, a trend in time series is characterized by the slope and duration, and its prediction is then to forecast the two values of the subsequent trend given historical data of the time series. For this problem, existing approaches mainly deal with the case in univariate time series. However, in many real-world applications, there are multiple variables at play, and handling all of them at the same time is crucial for an accurate prediction. A natural way is to employ multi-task learning (MTL) techniques in which the trend learning of each time series is treated as a task. The key point of MTL is to learn task relatedness to achieve better parameter sharing, which however is challenging in trend prediction task. First, effectively modeling the complex temporal patterns in different tasks is hard as the temporal and spatial dimensions are entangled. Second, the relatedness among tasks may change over time. In this paper, we propose a neural network, DeepTrends, for multivariate time series trend prediction. The core module of DeepTrends is a tensorized LSTM with adaptive shared memory (TLASM). TLASM employs the tensorized LSTM to model the temporal patterns of long-term trend sequences in an MTL setting. With an adaptive shared memory, TLASM is able to learn the relatedness among tasks adaptively, based upon which it can dynamically vary degrees of parameter sharing among tasks. To further consider short-term patterns, DeepTrends utilizes a multi-task 1dCNN to learn the local time series features, and employs a task-specific sub-network to learn a mixture of long-term and short-term patterns for trend prediction. Extensive experiments on real datasets demonstrate the effectiveness of the proposed model.