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 1351–1375 of 6748 papers

TitleStatusHype
Interpretable Classification of Early Stage Parkinson's Disease from EEG—0
Regular Time-series Generation using SGM—0
AI enabled RPM for Mental Health Facility—0
Dataset Bias in Human Activity Recognition—0
Deep Learning Enables Reduced Gadolinium Dose for Contrast-Enhanced Blood-Brain Barrier Opening—0
Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks—0
Probabilistic Traffic Forecasting with Dynamic RegressionCode0
Planning of Fast Charging Infrastructure for Electric Vehicles in a Distribution System and Prediction of Dynamic Price—0
Leveraging Vision-Language Models for Granular Market Change Prediction—0
ActSafe: Predicting Violations of Medical Temporal Constraints for Medication Adherence—0
Robust M-Estimation for Additive Single-Index Cointegrating Time Series Models—0
Sensor data-driven analysis for identification of causal relationships between exposure to air pollution and respiratory rate in asthmatics—0
Temporal-lobe Epilepsy: Harmonic and Anharmonic Periodicity in Microeletrode Voltage—0
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy—0
Ordinal methods for a characterization of evolving functional brain networks—0
Building a Fuel Moisture Model for the Coupled Fire-Atmosphere Model WRF-SFIRE from Data: From Kalman Filters to Recurrent Neural Networks—0
Neural Spline Search for Quantile Probabilistic Modeling—0
Unsupervised Driving Event Discovery Based on Vehicle CAN-data—0
LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification—0
Persistence-Based Discretization for Learning Discrete Event Systems from Time Series—0
A Novel Framework for Handling Sparse Data in Traffic Forecast—0
Experimental demonstration of bandwidth enhancement in photonic time delay reservoir computing—0
Learnable Path in Neural Controlled Differential EquationsCode0
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration—0
Application of machine learning to gas flaring—0
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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