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 5801–5825 of 6748 papers

TitleStatusHype
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition—0
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality DiscoveryCode0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data—0
Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding—0
Self-Similarity Based Time Warping—0
A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks—0
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks—0
Bidirectional deep-readout echo state networksCode0
Beyond Sparsity: Tree Regularization of Deep Models for InterpretabilityCode0
A Correlation Based Feature Representation for First-Person Activity RecognitionCode0
Learning to Predict with Highly Granular Temporal Data: Estimating individual behavioral profiles with smart meter data—0
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features—0
Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework—0
Sparsification of the Alignment Path Search Space in Dynamic Time Warping—0
Aggregated Wasserstein Metric and State Registration for Hidden Markov Models—0
Attend and Diagnose: Clinical Time Series Analysis using Attention Models—0
Interpretable Vector AutoRegressions with Exogenous Time Series—0
Long-Term Online Smoothing Prediction Using Expert Advice—0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data—0
DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data—0
Computer activity learning from system call time series—0
Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording—0
Channel masking for multivariate time series shapelets—0
Long-term Forecasting using Higher Order Tensor RNNsCode0
Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute—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