MGEoT: A Multi-grained Ensemble Method for Time Series Classification

A deep multi-task representation learning method for time series classification and retrieval

Throughout recent years, dynamic time warping (DTW) has remained as a robust similarity measure in time series classification (TSC). 1-nearest neighbor (1-NN) algorithm with DTW is the most widely used classification method on time series serving as a benchmark. With the increasing demand for TSC on low-resource devices and the widespread of wearable devices, the need for a efficient and accurate time series classifier has never been higher. Although 1-NN DTW attains accurate results, it highly falls back on efficiency due to its quadratic complexity in the length of time series. In this paper, we propose a new approximation method for reducing the length of the time series as the input of DTW. We call it control chart approximation (CCA), after a similar concept used in statistical quality control processing. CCA representation approximates raw time series by transforming them into a set of segments with aggregated values and durations forming a reduced 3-D vector. We also propose an adaptation of DTW in 3-D space as a distance measure for 1-NN classifier, and denote the method as 1-NN 3-D DTW. Our experiments on 85 datasets from UCR archive—including 28 long-length (>500 points) time series datasets—show up to two orders of magnitude performance gain in running time compared to the state-of-the-art 1-NN DTW implementation. Moreover, it shows similar or better accuracy on the long time series in the experiment.

Active learning has been widely used to select the most informative data for labeling in classification tasks, except for time series classification. The main challenge of active learning in time series classification is to evaluate the informativeness of a time series instance. Specifically, many informativeness metrics have been proposed for traditional active learning, however, none of them is particularly effective on time series data. In this paper, we design an informativeness metric that considers the characteristics of time series data in defining our instance uncertainty and utility. We prove that our informativeness metric is a submodular set function, and further develop an effective and efficient algorithm to select the most informative time series instances for training. In the experiment, we validate our method on a variety of datasets in the UCR Time Series Data Archive. The results show that our method achieves a higher classification accuracy than existing methods, using only 50% of the training instances.

Soft sensor for non-invasive detection of process events based on Eigenresponse Fuzzy Clustering

Time series data produced from various sensors are mostly different from each other in length due to various environment of collection. Most time series classification methods, however, assume that the lengths of time series data are the same. There is no alternative, moreover, for variant-length time series classification except DTW and DTW-related methods and few researches on transformation of time series length can be found in direct ways. In this paper, we propose a length transformation method for effective variant-length time series classification. Proposed method is a similarity-preserving transformation and the restoration is possible. To evaluate the proposed method, experiments are conducted to compare the classification performance by applying well-known methods to the time series data before and after the transformation. The classification using the dataset transformed by the proposed method shows better performance on almost all measurements.

Time series data is increasingly used in a wide range of fields, and it is often relied on in crucial applications and high-stakes decision-making. For instance, sensors generate time series data to recognize different types of anomalies through automatic decision-making systems. Typically, these systems are realized with machine learning models that achieve top-tier performance on time series classification tasks. Unfortunately, the logic behind their prediction is opaque and hard to understand from a human standpoint. Recently, we observed a consistent increase in the development of explanation methods for time series classification justifying the need to structure and review the field. In this work, we (a) present the first extensive literature review on Explainable AI (XAI) for time series classification, (b) categorize the research field through a taxonomy subdividing the methods into time points-based, subsequences-based and instance-based, and (c) identify open research directions research directions regarding the type of explanations and the evaluation of explanations and interpretability.

Classifying a time series is a fundamental task in temporal analysis. This provides valuable insights into the temporal characteristics of data. Although it has been applied to traffic flow and individual-centered accessibility analysis, it has yet to be applied to place-centered accessibility research. In this study, we have proposed an actual isochrone and dynamic time-wrapping distance-based k-medoids method and tested its applicability to a bus accessibility analysis. Using bus floating car data, our method calculated the actual isochrone area as an accessibility measurement and constructs an accessibility time series for each hexagonal geographical unit within the area of interest. We then calculated the dynamic time warp distance between the accessibility time series of pairwise geographical units and used these distances for k-medoid clustering. The optimized class number k was selected by considering the elbow method, silhouette score, and human examination. Our case study in Hefei, China demonstrates the feasibility of our method for accessibility time series classification. We also discovered that the resulting classes follow clear spatial patterns, indicating that different time series classes may be correlated with their spatial location. To our knowledge, this is the first time that such a classification method has been applied to place-centered accessibility time series analysis. Our data-driven method can inform place-centered accessibility in an era in which large quantities of spatiotemporal data like floating car data are available.

Application of selected supervised learning methods for time series classification in Building Automation and Control Systems

Time series classification using local distance-based features in multi-modal fusion networks

Convolutional Neural Networks such as U-Net are recently getting popular among researchers in many applications, such as Biomedical Image Segmentation. U-Net is one of the popular deep Convolutional Neural Networks which first contracts the input image using pooling layers and then upscales the feature maps before classifying them. In this paper, we explore the performance of adaptive scaling for U-Net in time series classification. Also, to improve performance, we extract features from the trained U-Net model and use ensemble deep Random Vector Functional Link (edRVFL) to classify them. Experiments on 55 large UCR datasets reveal that adaptive scaling improves the performance of U-Net in time series classification. Also, using edRVFL on extracted features from the trained U-Net model enhances performance. Consequently, our U-Net-edRVFL classifier outperforms other time series classification methods.

Traditional works on time series classification usually use all of data in time series without distinction. However, that will swamp the discriminative information and decrease the correctness of classification. In this paper, a feature segment based time series classification algorithm was proposed, which only selects some highly discriminative time series data for classification. Firstly, an adaptive time series segmentation method was proposed. Then, a large margin based feature segment selection method was given. Based on these two methods, a time series classification framework was established after representing the time series with the optimal segments. By exploring the discriminative temporal patterns hidden in subsequences of time series and giving them more emphasize, the algorithm proposed in this paper can improve the time series classification performance greatly. Extensive experimental results showed that the proposed algorithm can achieve a good classification performance.

Binary classification of floor vibrations for human activity detection based on dynamic mode decomposition

Ad-hoc explanation for time series classification

In the area of advanced human-computer interaction, automatic gesture recognition is an important field. Motion data produced by the accelerometer of a smart watch can be utilized in hand gesture recognition. In this work we examine the use of a commodity smart watch and a smartphone as the capture and the processing units respectively, for recognizing gestures. We claim that if the proper gesture recognition algorithms are applied, the recognition of natural gestures i.e. 3-D gestures easily performed by an individual can be accurate enough to be useful in everyday life activities. Symbolic Aggregate Approximation (SAX) and Dynamic Time Warping (DTW) methodologies are utilized in this context and evaluated using a set of six 3-D natural gestures.

Time-series classification is one of the most important machine learning tasks related to time series. It is the common denominator in various recognition tasks, such as signature verification, person identification based on keystroke dynamics, detection of cardiovascular diseases and brain disorders (e.g. early stage of Alzheimer disease or dementia). This tutorial aims to give an introduction to the most prominent challenges, methods, evaluation protocols and biomedical applications related to time series classification. Besides the "conventional" time series classification task, early classification and semi-supervised classification will be considered. Both preprocessing techniques -- FFT, SAX, etc. -- and wide-spread classifiers -- such as similarity-based, feature-based, motif/shaplet-based classifiers and convolutional neural networks -- will be covered. As dynamic time warping (DTW) is the one of the key components of many time series classifiers, including recent ones based on deep learning, we will describe this technique in detail. Slides: http://www.biointelligence.hu/pdf/timeseriestutorial.pdf