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 376–400 of 6748 papers

TitleStatusHype
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time SeriesCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
Early Abandoning and Pruning for Elastic Distances including Dynamic Time WarpingCode1
ClaSP - Time Series SegmentationCode1
General Evaluation for Instruction Conditioned Navigation using Dynamic Time WarpingCode1
An Evaluation of Change Point Detection AlgorithmsCode1
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural NetworksCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural NetworkCode1
Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization ApproachCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
Ensembles of Localised Models for Time Series ForecastingCode1
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting EpidemicsCode1
Changing Fashion CulturesCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
Euler State Networks: Non-dissipative Reservoir ComputingCode1
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly BenchmarkCode1
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time IntervalsCode1
Correlation-aware Unsupervised Change-point Detection via Graph Neural NetworksCode1
Exploring the Advantages of Transformers for High-Frequency TradingCode1
Expressing Multivariate Time Series as Graphs with Time Series Attention TransformerCode1
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network GenerationCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
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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