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 3526–3550 of 6748 papers

TitleStatusHype
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph—0
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data—0
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks—0
Parkinson's Disease Diagnosis Using Deep Learning—0
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network—0
Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19—0
Silicon Photonic Microring Based Chip-Scale Accelerator for Delayed Feedback Reservoir Computing—0
Leveraging latent persistency in United States patent and trademark applications to gain insight into the evolution of an innovation-driven economy—0
Latent Space Semi-Supervised Time Series Data Clustering—0
The 2020 Global Stock Market Crash: Endogenous or Exogenous?—0
Anomaly detection and regime searching in fitness-tracker data—0
A Multi-Modal and Multitask Benchmark in the Clinical Domain—0
Jumpy Recurrent Neural Networks—0
Anomaly detection in dynamical systems from measured time series—0
Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion—0
Recurrent Neural Network Architecture based on Dynamic Systems Theory for Data Driven Modelling of Complex Physical Systems—0
Detecting residues of cosmic events using residual neural network—0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data—0
Latent Convergent Cross Mapping—0
Generative Time-series Modeling with Fourier Flows—0
Graph Edit NetworksCode0
"Hey, that's not an ODE'": Faster ODE Adjoints with 12 Lines of Code—0
An Euler-based GAN for time series—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