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 4426–4450 of 6748 papers

TitleStatusHype
Investigating echo state networks dynamics by means of recurrence analysis—0
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals—0
Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey—0
Investigating the genomic background of CRISPR-Cas genomes for CRISPR-based antimicrobials—0
Investigating the impact of autocorrelation on time-varying connectivity—0
Investigation of Flash Crash via Topological Data Analysis—0
Investigation of Proper Orthogonal Decomposition for Echo State Networks—0
Investigation on the use of Hidden-Markov Models in automatic transcription of music—0
IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach—0
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
Irregularly-Sampled Time Series Modeling with Spline Networks—0
Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)—0
Isolating the impact of trading on grid frequency fluctuations—0
Is the Indian Stock Market efficient - A comprehensive study of Bombay Stock Exchange Indices—0
Is type 1 diabetes a chaotic phenomenon?—0
IT2CFNN: An Interval Type-2 Correlation-Aware Fuzzy Neural Network to Construct Non-Separable Fuzzy Rules with Uncertain and Adaptive Shapes for Nonlinear Function Approximation—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes—0
Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability—0
It's a super deal -- train recurrent network on noisy data and get smooth prediction free—0
It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]—0
Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data—0
Jerk-Aware Video Acceleration Magnification—0
Jiffy: A Convolutional Approach to Learning Time Series Similarity—0
Joint cardiac T_1 mapping and cardiac function estimation using a deep manifold framework—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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