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 4201–4225 of 6748 papers

TitleStatusHype
R-FORCE: Robust Learning for Random Recurrent Neural NetworksCode0
Spatio-Temporal Graph Convolution for Resting-State fMRI AnalysisCode1
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction—0
Scalable Deployment of AI Time-series Models for IoT—0
TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications—0
Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework—0
Generative ODE Modeling with Known UnknownsCode1
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes—0
The Internet of Things as a Deep Neural Network—0
Deep Markov Spatio-Temporal FactorizationCode0
TSFEL: Time Series Feature Extraction LibraryCode2
One model is not enough: heterogeneity in cryptocurrencies' multifractal profiles—0
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time—0
Locating line and node disturbances in networks of diffusively coupled dynamical agents—0
Human Activity Recognition from Wearable Sensor Data Using Self-AttentionCode1
Construe: a software solution for the explanation-based interpretation of time seriesCode1
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix—0
BrazilDAM: A Benchmark dataset for Tailings Dam DetectionCode0
Market states: A new understanding—0
Time series and machine learning to forecast the water quality from satellite data—0
Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting—0
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions—0
PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction—0
Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations—0
A CNN–LSTM model for gold price time-series forecasting—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