arXiv research

Temporal information impacts only a fraction of time series datasets, skewing benchmark evaluations.

The UCR Time Series Archive - introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone th…

Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10 thousand time series only; w…

Benchmark study evaluates 8 clustering methods on 99 UCR time series datasets.

In 2002, the UCR time series classification archive was first released with sixteen datasets. It gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In October 2018 more datasets were added, bringing the total to 128. The new archive contains a wide range of problems, including var…

Recent anomaly detection benchmarks are flawed, potentially misleading progress.

Introduces a new benchmark for time series extrinsic regression.

Adaptive weighting schemes enhance time-series data augmentation for financial and UCR datasets.

New study on time series anomaly detection shows overlapping inference improves performance.

Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of research. This paper investigates the use of transfer learning in Deep Neural Networks and a 2D representation of time series known as Recurrenc…

A new method extracts features from time series data using iterated sums and improves classification accuracy.

Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are cur…

Computer vision SSL methods show effectiveness on time series data.

RED CoMETS improves multivariate time series classification accuracy.

TimeVQVAE-AD detects anomalies in time series data with high accuracy and provides explainable results.

Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network's generalization capabilities …

MINIROCKET speeds up time series classification by 75x.

Enhanced Sampling Scheme improves masked generative modeling.

Guided warping augments time series data by aligning features with a teacher.

TimeVQVAE uses VQ for better time series generation.

RST improves environmental time series classification accuracy using randomized B-spline trees.

Archive of 20 time series datasets for forecasting evaluation.

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learnin…

LETS-GZSL tackles GZSL for time series classification, achieving high accuracy.

Paper proposes DTW-SOM for visual exploration of time-series motifs.

End-to-end model for time series classification with missing data.

Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each …

Dictionary based classifiers are a family of algorithms for time series classification (TSC), that focus on capturing the frequency of pattern occurrences in a time series. The ensemble based Bag of Symbolic Fourier Approximation Symbols (BOSS) was found to be a top performing TSC algorithm in a recent evaluation, as w…

NM-VQTSG improves synthetic time series fidelity by aligning distributions.

Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered practical scenario where access to labeled training data is difficult, and where DNNs would be prone to overfitting. We leverage recent advance…

There are now a broad range of time series classification (TSC) algorithms designed to exploit different representations of the data. These have been evaluated on a range of problems hosted at the UCR-UEA TSC Archive (www.timeseriesclassification.com), and there have been extensive comparative studies. However, our und…

Time Series Classification (TSC) has seen enormous progress over the last two decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is the current state of the art in terms of classification accuracy. HIVE-COTE recognizes that time series data are a specific data type for which the traditi…

An emended and improved version of the present paper has been archived in math-ph/0505057, and a preliminary account of its content has been published in Phys.Rev.Lett. 92, 60601, (2004). Moreover, in order to prove the relevance of topology for phase transition phenomena in a broad domain of physically interesting cas…

Co-eye combines multiple symbolic representations to improve time series classification accuracy.

New metrics fail adversarial tests, with some more robust than others.

Since the introduction and the public availability of the \textsc{ucr} time series benchmark data sets, numerous Time Series Classification (TSC) methods has been designed, evaluated and compared to each others. We suggest a critical view of TSC performance evaluation protocols put in place in recent TSC literature. Th…

VOLARE provides standardized realized volatility measures from financial data.

Extends Popularity Bias Memorization theorem to new conditions.

Polymarket-v1 Database tracks 1.2B trades across 1.3M markets with 100% ground-truth direction.

A new method sorts models to find the best one with minimal risk.

Describes automorphism group of Rauzy diagrams.

This is an review on the point classification of second order ODE's by Ruslan Sharipov. His works were published in 1997-1998 at the Electronic Archive at LANL and undeservedly forgotten. Last chapter is an application of this classification to the investigation of Painleve equations.

Fusion framework improves time series classification across different datasets.

New TSER algorithms outperform existing methods in time series extrinsic regression.

New algorithms benchmarked for multivariate time series classification.

Remote Sensing Images from satellites have been used in various domains for detecting and understanding structures on the ground surface. In this work, satellite images were used for localizing parking spaces and vehicles in parking lots for a given parcel using an RCNN based Neural Network Architectures. Parcel shapef…

We present in this paper experiments on Table Recognition in hand-written registry books. We first explain how the problem of row and column detection is modeled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation wa…

The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a single workstation with enabled NVIDIA GPU card that takes up to 1 hour to complete the training task and archive an overall average accuracy of $94.7\%$. The DNN …