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 4651–4675 of 6748 papers

TitleStatusHype
Linear Temporal Public Announcement Logic: a new perspective for reasoning about the knowledge of multi-classifiers—0
Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series—0
Linear-time online visibility graph transformation algorithm: for both natural and horizontal visibility criteria—0
Line Spectrum Representation for Vector Processes With Application to Frequency Estimation—0
Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition—0
Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests—0
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data—0
Liquid State Machine-Empowered Reflection Tracking in RIS-Aided THz Communications—0
Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation—0
Local approximation of operators—0
Local Exceptionality Detection in Time Series Using Subgroup Discovery—0
Locality and low-dimensions in the prediction of natural experience from fMRI—0
Localizing the Origin of Idiopathic Ventricular Arrhythmia from ECG Using an Attention-Based Recurrent Convolutional Neural Network—0
Locally Adaptive Bayesian Multivariate Time Series—0
Locally adaptive factor processes for multivariate time series—0
Locally embedded presages of global network bursts—0
Locally Linear Embedding and fMRI feature selection in psychiatric classification—0
Local Polynomial Estimation of Time-Varying Parameters in Nonlinear Models—0
Local Score Dependent Model Explanation for Time Dependent Covariates—0
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality—0
Locating Changes in Highly Dependent Data with Unknown Number of Change Points—0
Logic-based Clustering and Learning for Time-Series Data—0
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts—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