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 4001–4025 of 6748 papers

TitleStatusHype
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture—0
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal DataCode0
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons—0
Lasso Inference for High-Dimensional Time Series—0
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variablesCode0
MAGMA: Inference and Prediction with Multi-Task Gaussian ProcessesCode0
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for BrazilCode0
Permutation-based tests for discontinuities in event studies—0
Time Series Source Separation with Slow Flows—0
Machine Learning a Molecular Hamiltonian for Predicting Electron Dynamics—0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach—0
Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?—0
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles—0
A fast noise filtering algorithm for time series prediction using recurrent neural networks—0
Leveraging the Self-Transition Probability of Ordinal Pattern Transition Graph for Transportation Mode ClassificationCode0
Attention Mechanism for Multivariate Time Series Recurrent Model Interpretability Applied to the Ironmaking Industry—0
MTS-CycleGAN: An Adversarial-based Deep Mapping Learning Network for Multivariate Time Series Domain Adaptation Applied to the Ironmaking Industry—0
Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis—0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry—0
SRDCNN: Strongly Regularized Deep Convolution Neural Network Architecture for Time-series Sensor Signal Classification Tasks—0
Generating Trading Signals by ML algorithms or time series ones?—0
GeoStat Representations of Time Series for Fast Classification—0
A unified machine learning approach to time series forecasting applied to demand at emergency departments—0
Rewiring the Transformer with Depth-Wise LSTMs—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