Time Series Classification with Discrete Wavelet Transformed Data

Li, Daoyuan; Bissyandé, Tegawendé F.; Klein, Jacques; Traon, Yves Le

doi:10.1142/s0218194016400088

Cited by 22 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid Models. Daoyuan et al [11] intended a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other.…”

Section: Literature Reviewmentioning

confidence: 99%

A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification

Bahuguna

Yadav

Senapati

et al. 2022

IFS

View full text Add to dashboard Cite

Covid-19 braces serious mental health crisis across the world. Since a vast majority of the population exploit social media platforms such as twitter to exchange information, rapid collecting anf analyzing social media data to understand personal well-being and subsequently adopting adequate measures could avoid severe socio-economic damage. Sentiment analysis on twitter data is very useful to understand and identify the mental health issues. In this research, we proposed a unified deep neuro-fuzzy approach for Covid-19 twitter sentiment classification. Fuzzy logic has been a very powerful tool for twitter data analysis where approximate semantic and syntactic analysis is more relevant because correcting spelling and grammar in tweets are merely obnoxious. We conducted the experiment on three challenging COVID-19 twitter sentiment datasets. Experimental results demonstrate that fuzzy Sugeno integral based ensembled classifers succeed over individual base classifiers.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification

Bahuguna

Yadav

Senapati

et al. 2022

IFS

View full text Add to dashboard Cite

show abstract

“…Classical approaches of classifying financial time-series adopt the principles from physics perspective and focus on mathematical derivation. For example, wavelets (e.g., [16,17]) and Fourier transform (e.g., [18]) are used to identify the signals in the frequency domain. Distance-based methods coupled with similarity metrics are another popular approach in time-series classification, such as the nearest neighbour classifier with the dynamic time warping distance function [19].…”

Section: Related Workmentioning

confidence: 99%

Denoised Labels for Financial Time-Series Data via Self-Supervised Learning

Ma¹,

Ventre²,

Polukarov³

2021

Preprint

View full text Add to dashboard Cite

The introduction of electronic trading platforms effectively changed the organisation of traditional systemic trading from quote-driven markets into order-driven markets. Its convenience led to an exponentially increasing amount of financial data, which is however hard to use for the prediction of future prices, due to the low signal-to-noise ratio and the non-stationarity of financial time-series. Simpler classification tasks -where the goal is to predict the directions of future price movement -via supervised learning algorithms, need sufficiently reliable labels to generalise well. Labelling financial data is however less well defined than other domains: did the price go up because of noise or because of signal? The existing labelling methods have limited countermeasures against noise and limited effects in improving learning algorithms. This work takes inspiration from the image classification in trading [1] and success in self-supervised learning (e.g., [2]). We investigate the idea of applying computer vision techniques to financial time-series to reduce the noise exposure and hence generate correct labels. We look at the label generation as the pretext task of a self-supervised learning approach and compare the naive (and noisy) labels, commonly used in the literature, with the labels generated by a denoising autoencoder for the same downstream classification task. Our results show that our denoised labels improve the performances of the downstream learning algorithm, for both small and large datasets. We further show that the signals we obtain can be used to effectively trade with binary strategies [3]. We suggest that with proposed techniques, self-supervised learning constitutes a powerful framework for generating "better" financial labels that are useful for studying the underlying patterns of the market.

show abstract

“…With a large set of time series data, analysis tasks would face certain challenges in defining matching features; therefore, taking advantage of wavelet decomposition to reduce the dimensionality of data is beneficial [127]. The classification task can be accurately performed utilizing the discrete wavelet transforms technique [128].…”

Section: B Feature Extractionmentioning

confidence: 99%

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

et al. 2019

View full text Add to dashboard Cite

Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics.

show abstract

Time Series Classification with Discrete Wavelet Transformed Data

Cited by 22 publications

References 26 publications

A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification

A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification

Denoised Labels for Financial Time-Series Data via Self-Supervised Learning

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

Contact Info

Product

Resources

About