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 4426–4450 of 6748 papers

TitleStatusHype
Semantic Discord: Finding Unusual Local Patterns for Time SeriesCode0
Towards a Kernel based Uncertainty Decomposition Framework for Data and Models—0
Automated Deep Abstractions for Stochastic Chemical Reaction NetworksCode0
A Time-Series Distribution Test System Based on Real Utility Data—0
Signatures of brain criticality unveiled by maximum entropy analysis across cortical states—0
Ensemble Grammar Induction For Detecting Anomalies in Time Series—0
WISDoM: characterizing neurological timeseries with the Wishart distribution—0
Analysis, Online Estimation, and Validation of a Competing Virus Model—0
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information—0
Dynamic clustering of time series data—0
Machine Learning for a Music Glove Instrument—0
Bayesian nonparametric shared multi-sequence time series segmentation—0
Multi-label Prediction in Time Series Data using Deep Neural Networks—0
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methodsCode0
Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning—0
Semi-metric portfolio optimization: a new algorithm reducing simultaneous asset shocks—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture modelsCode0
Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings—0
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting—0
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data—0
A Multi-Scale Tensor Network Architecture for Classification and Regression—0
Causality based Feature Fusion for Brain Neuro-Developmental Analysis—0
Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification—0
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems—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