The dictionary-based approach is one of the most representative types of time series classification (TSC) algorithm due to its high accuracy, efficiency, and good interpretability. However, existing studies focus on the centralized scenario where data from multiple sources are gathered. Considering that in many practical applications, data owners are reluctant to share their data due to privacy concerns, we study an unexplored problem involving collaboratively building the dictionary-based model over the data owners without disclosing their private data (i.e., in the federated scenario). We propose FedDict, a novel dictionarybased TSC approach customized for the federated setting to benefit from the advantages of the centralized algorithms. To further improve the performance and practicality, we propose a novel federated optimization algorithm for training logistic regression classifiers using dictionary features. The algorithm does not rely on any secure broker and is more accurate and efficient than existing solutions without hyper-parameter tuning. We also propose two contract algorithms for federated dictionary building, such that the user can flexibly balance the running time and the TSC performance through a predefined time limit. Extensive experiments on a total of 117 highly heterogeneous datasets validate the effectiveness of our methods and the superiority over existing solutions.

Unsupervised (a.k.a. Self-supervised) representation learning (URL) has emerged as a new paradigm for time series analysis, because it has the ability to learn generalizable time series representation beneficial for many downstream tasks without using labels that are usually difficult to obtain. Considering that existing approaches have limitations in the design of the representation encoder and the learning objective, we have proposed Contrastive Shapelet Learning (CSL), the first URL method that learns the general-purpose shapelet-based representation through unsupervised contrastive learning, and shown its superior performance in several analysis tasks, such as time series classification, clustering, and anomaly detection. In this paper, we develop TimeCSL, an end-to-end system that makes full use of the general and interpretable shapelets learned by CSL to achieve explorable time series analysis in a unified pipeline. We introduce the system components and demonstrate how users interact with TimeCSL to solve different analysis tasks in the unified pipeline, and gain insight into their time series by exploring the learned shapelets and representation.

In recent years, the problem of traffic congestion has become a hot topic. Accurate traffic flow prediction methods have received extensive attention from many researchers all over the world. Although many methods proposed at present have achieved good results in the field of traffic flow prediction, most of them only consider the static characteristic of traffic data, but do not consider the dynamic characteristic of traffic data. The factors that affect traffic flow prediction are changeable, and they will change over time. In response to this dynamic characteristic, the authors propose a model fusion mechanism based on transformer (TransFusion). The authors adopt two basic forecasting models (TCN and LSTM) as the underlying architectures. In view of the performance of different models on the traffic data at different times, the authors design a model fusion mechanism to assign dynamic weights to basic models at different times. Experiments on three datasets have proved that TransFusion has a significant improvement compared with basic models.