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 6076–6100 of 6748 papers

TitleStatusHype
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets—0
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case—0
Fast and Accurate Time Series Classification with WEASELCode0
Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces—0
Topology data analysis of critical transitions in financial networks—0
Integrated information and dimensionality in continuous attractor dynamics—0
Temporal Overdrive Recurrent Neural Network—0
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation—0
Towards prediction of rapid intensification in tropical cyclones with recurrent neural networks—0
Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition—0
Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review—0
See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS—0
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory—0
Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 2 Validation—0
Deep Learning for Time-Series Analysis—0
Detecting changes in slope with an L_0 penalty—0
Evaluating Preprocessing Strategies for Time Series Prediction Using Deep Learning Architectures—0
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
Information theoretical study of cross-talk mediated signal transduction in MAPK pathways—0
Deep Neural Networks to Enable Real-time Multimessenger Astrophysics—0
A Basic Recurrent Neural Network Model—0
Inference of Causal Information Flow in Collective Animal Behavior—0
A Method for Massively Parallel Analysis of Time Series—0
Unsupervised Learning for Computational PhenotypingCode0
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains—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