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Digital Filters
One of the main purposes of digital filtering is to improve the quality of the signal. In this chapter, we give an overview of digital filtering....
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The role of classifiers and data complexity in learned Bloom filters: insights and recommendations
Bloom filters, since their introduction over 50 years ago, have become a pillar to handle membership queries in small space, with relevant...
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Equivariance-Based Analysis of PDE Evolutions Related to Multivariate Medians
For multivariate data there exist several concepts generalising the median, which differ by their equivariance properties w.r.t. transformations of...
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Evaluating Explanation Methods for Multivariate Time Series Classification
Multivariate time series classification is an important computational task arising in applications where data is recorded over time and over multiple...
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Towards Improving Multivariate Time-Series Forecasting Using Weighted Linear Stacking
In this day and age, the emergence of Big Data, has made a substantial amount of data accessible across various fields. In particular, time-series...
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Adaptive online variance estimation in particle filters: the ALVar estimator
We present a new approach—the
ALVar estimator—to estimation of asymptotic variance in sequential Monte Carlo methods, or, particle filters. The...
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RED CoMETS: An Ensemble Classifier for Symbolically Represented Multivariate Time Series
Multivariate time series classification is a rapidly growing research field with practical applications in finance, healthcare, engineering, and...
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Improving position encoding of transformers for multivariate time series classification
Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require...
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Multivariate sequence prediction for graph convolutional networks based on ESMD and transfer entropy
Multivariate time series modeling has been an important topic of interest for researchers in various fields. However, most of the existing methods...
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XCTF: A CNN-Based Interpretable Model for Multivariate Time Series Forecasting
Over the past decade, multivariate time series forecasting is becoming a research hotspot. Despite the emergence of several deep learning-based...
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Deep transition network with gating mechanism for multivariate time series forecasting
As an essential task in the machine learning community, multivariate time series forecasting has many real-world applications, such as PM2.5...
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Universal representation learning for multivariate time series using the instance-level and cluster-level supervised contrastive learning
The multivariate time series classification (MTSC) task aims to predict a class label for a given time series. Recently, modern deep learning-based...
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Attentional Gated Res2Net for Multivariate Time Series Classification
Multivariate time series classification is a critical problem in data mining with broad applications. It requires harnessing the inter-relationship...
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Structure-aware decoupled imputation network for multivariate time series
Handling incomplete multivariate time series is an important and fundamental concern for a variety of domains. Existing time-series imputation...
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A Critical Analysis of Classifier Selection in Learned Bloom Filters: The Essentials
It is well known that Bloom Filters have a performance essentially independent of the data used to query the filters themselves, but this is no more...
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A two-stage adversarial Transformer based approach for multivariate industrial time series anomaly detection
Sensors in complex industrial systems generate multivariate time series data, frequently leading to diverse abnormal patterns that pose challenges...
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TDG4MSF: A temporal decomposition enhanced graph neural network for multivariate time series forecasting
Multivariate time series forecasting is an important issue in industries, agriculture, finance, and other applications. There are many challenging...
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Probabilistic autoencoder with multi-scale feature extraction for multivariate time series anomaly detection
Effectively detecting anomalies for multivariate time series is of great importance for the modern industrial system. Recently, reconstruction-based...
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Dimension Selection Strategies for Multivariate Time Series Classification with HIVE-COTEv2.0
Multivariate time series classification (MTSC) is an area of machine learning that deals with predicting a discrete target variable from...
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A Comparative Study of Univariate and Multivariate Time Series Forecasting for CPO Prices Using Machine Learning Techniques
The Malaysian palm oil sector has significantly contributed to develo** the domestic economy and the global palm oil market. However, the...