A review on distance based time series classification

Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to its simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.TIN2016-78365-

Ad-Hoc Explanation for Time Series Classification

In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and specific for time series. In real-world time series, variations in the speed or the scale of a particular action, for instance, may determine the class, so modifying this type of characteristic leads to ad-hoc explanations for time series. To this end, four perturbations or transformations are proposed: warp, scale, noise, and slice. Given a transformation, an interval of a series is considered relevant for the prediction of a classifier if a transformation in this interval changes the prediction. Another novelty is that the method provides a two-level explanation: a high-level explanation, where the robustness of the prediction with respect to a particular transformation is measured, and a low-level explanation, where the relevance of each region of the time series in the prediction is visualized. In order to analyze and validate our proposal, first some illustrative examples are provided, and then a thorough quantitative evaluation is carried out using a specifically designed evaluation procedure.PID2019-104966GB-I00 3KIA-KK2020/004

Time Series Classifier Recommendation by a Meta-Learning Approach

This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set and best classifier. For this, an ad-hoc set of quick estimators of the accuracies of the candidate classifiers (landmarkers) are designed, which are used as predictors for the recommendation system. The performance of our recommender is compared with the performance of a standard method for non-sequential data and a set of baseline methods, which our method outperforms in 7 of the 9 considered scenarios. Since some meta-targets can be inferred from the predictions of other more fine-grained meta-targets, the last part of the work addresses the hierarchical inference of meta-targets. The experimentation suggests that, in many cases, a single model is sufficient to output many types of meta-targets with competitive results

Computer Integrated Manufacturing and Employment: Methodological Problems of Estimating the Employment Effects of CIM Application on the Macroeconomic Level

This paper is one of the first research products of the newly established Computer Integrated Manufacturing (CIM) Project, of which Prof. Ayres is the leader. It addresses issues of occupation-by-sector data availability, international comparability, and suitability for use with formal I-O models. Methods of estimating labor substitutability by CIM are also discussed, along with some early estimates of the impact of robotics on employment. The paper was formally presented at a session of the American Economic Association meeting in New Orleans, December 30, 1986. As an IIASA working paper it will be available to collaborating researchers and institutions in other countries

Early classification of time series by simultaneously optimizing the accuracy and earliness

The problem of early classi cation of time series appears naturally in contexts where the data, of temporal nature, is collected over time, and early class predictions are interesting or even required. The objective is to classify the incoming sequence as soon as possible, while maintaining suitable levels of accuracy in the predictions. Thus, we can say that the problem of early classi cation consists in optimizing two objectives simultaneously: accuracy and earliness. In this context, we present a method for early classi cation of time series based on combining a set of probabilistic classi ers together with a stopping rule. This stopping rule will act as a trigger and will tell us when to output a prediction or when to wait for more data, and it's main novelty lies in the fact that it is built by explicitly optimizing a cost function based on accuracy and earliness. We have selected a large set of benchmark datasets and 4 other state-of- the-art early classi cation methods and we have evaluated and compared our framework obtaining superior results in terms of both earliness and accuracy.TIN2016-78365-R, IT-609-1

Mutual information based feature subset selection in multivariate time series classification

This paper deals with supervised classification of multivariate time se- ries. In particular, the goal is to propose a filter method to select a subset of time series. Consequently, we adopt the framework proposed by Brown et al. [10]. The key point in this framework is the computation of the mutual information between the features, which allows us to measure the relevance of each feature subset. In our case, where the features are a time series, we use an adaptation of existing nonparametric mutual infor- mation estimators based on the k-nearest neighbor. Specifically, for the purpose of bringing these methods to the time series scenario, we rely on the use of dynamic time warping dissimilarity. Our experimental results show that our method is able to strongly reduce the number of time series while keeping or increasing the classification accuracy.Grant agreement no. KK-2019/00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

Early classification of time series using multi-objective optimization techniques

In early classification of time series the objective is to build models which are able to make class-predictions for time series as accurately and as early as possible, when only a part of the series is available. It is logical to think that accuracy and earliness are conflicting objectives, since the more we wait, more data points from the series are available, and it is easier to make accurate class-predictions. Con- sidering this, the problem can be very naturally formulated as a multi-objective optimization problem, and solved as such. However, the solutions proposed in the literature up to now, reduce the problem into a single-objective problem by com- bining both objectives somehow. In this paper, we present a novel multi-objective formulation of the problem of early classification, and we design a solution us- ing multi-objective optimization techniques. This method will provide a variety of solutions which find different trade-offs between both objectives, allowing the user to select the most suitable solution a-posteriori, depending on the accuracy and earliness requirements of the problem at hand. To prove the usefulness of our proposal, we carry out an extensive experimentation process using 45 benchmark databases and we present a case study in the financial domain

Chandra X-Ray Study of Galactic Supernova Remnant G299.2-2.9

We report on observations of the Galactic supernova remnant (SNR) G299.2$-$2.9 with the {\it Chandra X-Ray Observatory}. The high resolution images with {\it Chandra} resolve the X-ray-bright knots, shell, and diffuse emission extending beyond the bright shell. Interior to the X-ray shell is faint diffuse emission occupying the central regions of the SNR. Spatially-resolved spectroscopy indicates a large foreground absorption ($N_{\rm H}$ $\sim$ 3.5 $\times$ 10$^{21}$ cm$^{-2}$), which supports a relatively distant location ($d$ $\sim$ 5 kpc) for the SNR. The blast wave is encountering a highly inhomogeneous ambient medium with the densities ranging over more than an order of magnitude ($n_0$ $\sim$ 0.1 $-$ 4 cm$^{-3}$). Assuming the distance of $d$ $\sim$ 5 kpc, we derive a Sedov age of $\tau$ $\sim$ 4500 yr and an explosion energy of $E_0$ $\sim$ 1.6 $\times$ 10$^{50}$ ergs. The ambient density structure and the overall morphology suggest that G299.2$-$2.9 may be a limb-brightened partial shell extending to $\sim$7 pc radius surrounded by fainter emission extending beyond that to a radius of $\sim$9 pc. This suggests the SNR exploded in a region of space where there is a density gradient whose direction lies roughly along the line of sight. The faint central region shows strong line emission from heavy elements of Si and Fe, which is caused by the presence of the overabundant stellar ejecta there. We find no evidence for stellar ejecta enriched in light elements of O and Ne. The observed abundance structure of the metal-rich ejecta supports a Type Ia origin for G299.2$-$2.9.Comment: 16 pages (AASTex emulator style), 3 Tables, 10 Figures (including 1 color: Figure 1), Accepted by Ap

A Review on Outlier/Anomaly Detection in Time Series Data

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.KK/2019-00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

Water leak detection using self-supervised time series classification

Leaks in water distribution networks cause a loss of water that needs to be com- pensated to ensure a continuous supply for all customers. This compensation is achieved by increasing the flow of the network, which entails an undesirable economical expense as well as negative consequences for the environment. For these reasons, detecting and fixing leaks is a relevant task for water distribution companies. This paper proposes a water leak detection method based on a self- supervised classification of flow time series. The aim is to detect the leaks in the network, providing a low false positive rate. The proposed method is applied to two water distribution networks and compared to two other methods in the literature, obtaining the best balance between the number of false positives and detected leaks.IT1244-19 PID2019-104966GB-I0