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Found 18 papers, 8 papers with code
VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection
Anomaly detection (AD) is a fundamental task for time-series analytics with important implications for the downstream performance of many applications.
$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
Time series clustering poses a significant challenge with diverse applications across domains.
Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art
Vector data is prevalent across business and scientific applications, and its popularity is growing with the proliferation of learned embeddings.
Dive into Time-Series Anomaly Detection: A Decade Review
Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics.
Data Quality Awareness: A Journey from Traditional Data Management to Data Science Systems
In this paper, we present a comprehensive review of the evolution of data quality (DQ) awareness from traditional data management systems to modern data-driven AI systems, which are integral to data science.
User-friendly Foundation Model Adapters for Multivariate Time Series Classification
Foundation models, while highly effective, are often resource-intensive, requiring substantial inference time and memory.
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensional, non-Gaussian data distributions.
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting.
Ranked #20 on
Time Series Forecasting
on ETTh1 (336) Multivariate
ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series
The experimental results with two large real datasets show that the proposed approach outperforms current solutions, including state-of-the-art time series classifiers applied to appliance detection.
Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting
Our optimal model can retain up to $92. 02\%$ the performance of the original forecasting model in terms of Mean Squared Error (MSE) on clean data, while being more robust than the standard adversarially trained models on perturbed data.
A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma
In digital histopathology, entire neoplasm segmentation on Whole Slide Image (WSI) of Hepatocellular Carcinoma (HCC) plays an important role, especially as a preprocessing filter to automatically exclude healthy tissue, in histological molecular correlations mining and other downstream histopathological tasks.
A Critical Re-evaluation of Benchmark Datasets for (Deep) Learning-Based Matching Algorithms
Entity resolution (ER) is the process of identifying records that refer to the same entities within one or across multiple databases.
Appliance Detection Using Very Low-Frequency Smart Meter Time Series
This paper presents an in-depth evaluation and comparison of state-of-the-art time series classifiers applied to detecting the presence/absence of diverse appliances in very low-frequency smart meter data.
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
Convolutional neural networks perform well for the data series classification task; though, the explanations provided by this type of algorithm are poor for the specific case of multivariate data series.
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series
Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains.
SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality
The opinion expressed in various Web sites and social-media is an essential contributor to the decision making process of several organizations.
Progressive Data Science: Potential and Challenges
We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.
Node Classification in Uncertain Graphs
We study the novel problem of node classification in uncertain graphs, by treating uncertainty as a first-class citizen.
