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 5026–5050 of 6748 papers

TitleStatusHype
Multivariate Wasserstein Functional Connectivity for Autism Screening—0
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition—0
Multi-view Kernel PCA for Time series Forecasting—0
Correlative Channel-Aware Fusion for Multi-View Time Series Classification—0
Multi-Year Vector Dynamic Time Warping Based Crop Mapping—0
Muscle Vision: Real Time Keypoint Based Pose Classification of Physical Exercises—0
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis—0
MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction—0
Mutation frequency time series reveal complex mixtures of clones in the world-wide SARS-CoV-2 viral population—0
Mutual Information and the Edge of Chaos in Reservoir Computers—0
\#mygoal: Finding Motivations on Twitter—0
Myopia prediction for adolescents via time-aware deep learning—0
Named-Entity Based Sentiment Analysis of Nepali News Media Texts—0
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?—0
Near Optimal Heteroscedastic Regression with Symbiotic Learning—0
Near-optimal inference in adaptive linear regression—0
Necessary and sufficient conditions for causal feature selection in time series with latent common causes—0
Leverage, Endogenous Unbalanced Growth, and Asset Price Bubbles—0
Neighbor-encoder—0
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets—0
NetRCA: An Effective Network Fault Cause Localization Algorithm—0
Network Anomaly Detection based on Tensor Decomposition—0
Network Clustering Via Kernel-ARMA Modeling and the Grassmannian The Brain-Network Case—0
Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network—0
Network inference via process motifs for lagged correlation in linear stochastic processes—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