SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3026–3050 of 6748 papers

TitleStatusHype
Learning low-dimensional state embeddings and metastable clusters from time series data—0
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles—0
Heterogeneous Relational Kernel Learning—0
Differential Recurrent Neural Network and its Application for Human Activity Recognition—0
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?—0
From Generalization Analysis to Optimization Designs for State Space Models—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis—0
From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning—0
Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets—0
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers—0
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals—0
Frequency Domain Compact 3D Convolutional Neural Networks—0
Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression—0
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding—0
Arm order recognition in multi-armed bandit problem with laser chaos time series—0
A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society—0
Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances—0
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series—0
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network—0
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems—0
Free congruence: an exploration of expanded similarity measures for time series data—0
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization—0
Computer activity learning from system call time series—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified