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 2351–2375 of 6748 papers

TitleStatusHype
Improving the Thermal Infrared Monitoring of Volcanoes: A Deep Learning Approach for Intermittent Image Series—0
Time Series Model Attribution Visualizations as Explanations—0
Fully Spiking Variational AutoencoderCode1
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI ModellingCode1
Bitcoin Volatility and Intrinsic Time Using Double Subordinated Levy Processes—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—0
System Identification in Multi-Actuator Hard Disk Drives with Colored Noises using Observer/Kalman Filter Identification (OKID) Framework—0
Improving the spectral resolution of fMRI signals through the temporal de-correlation approach—0
Influence of Mobility Restrictions on Transmission of COVID-19 in the state of Maryland -- the USA—0
Long-Range Transformers for Dynamic Spatiotemporal ForecastingCode1
Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM—0
Modeling of Low Rank Time Series—0
Distributed Estimation of Sparse Inverse Covariances—0
Temporal Convolutional Attention Neural Networks for Time Series ForecastingCode1
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time SeriesCode1
Deep Learning with Kernel Flow Regularization for Time Series Forecasting—0
High-dimensional regression with potential prior information on variable importanceCode0
Analysis of chaotic dynamical systems with autoencoders—0
A Wavelet Method for Panel Models with Jump Discontinuities in the ParametersCode0
Learning Predictive and Interpretable Timeseries Summaries from ICU Data—0
Quantile-based fuzzy C-means clustering of multivariate time series: Robust techniques—0
Rotor Localization and Phase Mapping of Cardiac Excitation Waves using Deep Neural Networks—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Personalized Online Machine Learning—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