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 3151–3175 of 6748 papers

TitleStatusHype
Can Predominant Credible Information Suppress Misinformation in Crises? Empirical Studies of Tweets Related to Prevention Measures during COVID-19—0
Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting—0
MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationCode1
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting—0
Classification Models for Partially Ordered Sequences—0
Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff Modeling based on Recurrent Neural Networks—0
Time Series (re)sampling using Generative Adversarial Networks—0
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model—0
Deep Generative SToRM model for dynamic imaging—0
Gesture Recognition in Robotic Surgery: a Review—0
Adaptive Sequential Design for a Single Time-Series—0
Mining the Mind: Linear Discriminant Analysis of MEG source reconstruction time series supports dynamic changes in deep brain regions during meditation sessions—0
Low Dimensional Convolutional Neural Network For Solar Flares GOES Time Series ClassificationCode0
Reservoir Computing with Magnetic Thin Films—0
Low Rank Forecasting—0
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting—0
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series ForecastingCode2
Inference of stochastic time series with missing data—0
Adjusting for Autocorrelated Errors in Neural Networks for Time SeriesCode1
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
Statistical guided-waves-based SHM via stochastic non-parametric time series models—0
Indian Economy and Nighttime Lights—0
Echo State Network for two-dimensional turbulent moist Rayleigh-Bénard convection—0
Identification of brain states, transitions, and communities using functional MRI—0
Short-term prediction of Time Series based on bounding techniques—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