A Dictionary-based approach to Time Series Ordinal Classification

• URL: http://arxiv.org/abs/2305.09288v1
• Date: Tue, 16 May 2023 08:48:36 GMT
• Title: A Dictionary-based approach to Time Series Ordinal Classification
• Authors: Rafael Ayll\'on-Gavil\'an, David Guijo-Rubio, Pedro Antonio Guti\'errez and C\'esar Herv\'as-Martinez
• Abstract summary: We present an ordinal adaptation of the TDE algorithm, known as ordinal TDE (O-TDE) Experiments conducted show the improvement achieved by the ordinal dictionary-based approach in comparison to four other existing nominal dictionary-based techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-called dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the current state-of-the-art dictionary-based TSC approach. In many TSC problems we find a natural ordering in the labels associated with the time series. This characteristic is referred to as ordinality, and can be exploited to improve the methods performance. The area dealing with ordinal time series is the Time Series Ordinal Classification (TSOC) field, which is yet unexplored. In this work, we present an ordinal adaptation of the TDE algorithm, known as ordinal TDE (O-TDE). For this, a comprehensive comparison using a set of 18 TSOC problems is performed. Experiments conducted show the improvement achieved by the ordinal dictionary-based approach in comparison to four other existing nominal dictionary-based techniques.

Related papers

• On time series clustering with k-means [0.5530212768657544]
  Time series clustering algorithms are often presented with k-means configured in various ways. This variability makes it difficult to compare studies because k-means is known to be highly sensitive to its configuration. We propose a standard Lloyd's-based model for TSCL that adopts an end-to-end approach.
  arXiv  Detail & Related papers  (2024-10-18T08:24:07Z)
• SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking [89.43370214059955]
  Open-vocabulary Multiple Object Tracking (MOT) aims to generalize trackers to novel categories not in the training set. We present a unified framework that jointly considers semantics, location, and appearance priors in the early steps of association. Our method eliminates complex post-processings for fusing different cues and boosts the association performance significantly for large-scale open-vocabulary tracking.
  arXiv  Detail & Related papers  (2024-09-17T14:36:58Z)
• Regularization-Based Methods for Ordinal Quantification [49.606912965922504]
  We study the ordinal case, i.e., the case in which a total order is defined on the set of n>2 classes. We propose a novel class of regularized OQ algorithms, which outperforms existing algorithms in our experiments.
  arXiv  Detail & Related papers  (2023-10-13T16:04:06Z)
• Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering [8.14208923345076]
  The dynamic time warping (DTW) is commonly used for dealing with temporal delays, scaling, transformation, and many other kinds of temporal misalignments issues. We propose a generalized time warping invariant dictionary learning algorithm in this paper. The superiority of the proposed method in terms of dictionary learning, classification, and clustering is validated through ten sets of public datasets.
  arXiv  Detail & Related papers  (2023-06-30T14:18:13Z)
• Convolutional and Deep Learning based techniques for Time Series Ordinal Classification [7.047582157120573]
  Time Series Ordinal Classification (TSOC) is the field covering this gap, yet unexplored in the literature. This paper presents a first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art.
  arXiv  Detail & Related papers  (2023-06-16T11:57:11Z)
• On Pitfalls of Test-Time Adaptation [82.8392232222119]
  Test-Time Adaptation (TTA) has emerged as a promising approach for tackling the robustness challenge under distribution shifts. We present TTAB, a test-time adaptation benchmark that encompasses ten state-of-the-art algorithms, a diverse array of distribution shifts, and two evaluation protocols.
  arXiv  Detail & Related papers  (2023-06-06T09:35:29Z)
• Early Time-Series Classification Algorithms: An Empirical Comparison [59.82930053437851]
  Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. We evaluate six existing ETSC algorithms on publicly available data, as well as on two newly introduced datasets.
  arXiv  Detail & Related papers  (2022-03-03T10:43:56Z)
• The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier [0.0]
  We look at whether the complexity of the algorithms considered state of the art is really necessary. Many times the first approach suggested is a simple pipeline of summary statistics or other time series feature extraction approaches. We test these approaches on the UCR time series dataset archive, looking to see if TSC literature has overlooked the effectiveness of these approaches.
  arXiv  Detail & Related papers  (2022-01-28T11:23:58Z)
• PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method [49.93717224277131]
  We propose a new ETD method, called PER-ETD (i.e., PEriodically Restarted-ETD), which restarts and updates the follow-on trace only for a finite period. We show that PER-ETD converges to the same desirable fixed point as ETD, but improves the exponential sample complexity to bes.
  arXiv  Detail & Related papers  (2021-10-13T17:40:12Z)
• The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification [0.0]
  temporal dictionary ensemble (TDE) is more accurate than other dictionary based approaches. We show HIVE-COTE is significantly more accurate than the current best deep learning approach. This advance represents a new state of the art for time series classification.
  arXiv  Detail & Related papers  (2021-05-09T05:27:42Z)
• Benchmarking Multivariate Time Series Classification Algorithms [69.12151492736524]
  Time Series Classification (TSC) involved building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the previous state of the art. We review recently proposed bespoke MTSC algorithms based on deep learning, shapelets and bag of words approaches.
  arXiv  Detail & Related papers  (2020-07-26T15:56:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.