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 5351–5375 of 6748 papers

TitleStatusHype
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
Physics-Aware Gaussian Processes in Remote Sensing—0
Physics-aware, probabilistic model order reduction with guaranteed stability—0
Physics-informed generative neural network: an application to troposphere temperature prediction—0
Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting—0
Physics-informed machine learning for sensor fault detection with flight test data—0
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning—0
Physics-inspired machine learning for power grid frequency modelling—0
PhysioZoo: The Open Digital Physiological Biomarkers Resource—0
Piece-wise Matching Layer in Representation Learning for ECG Classification—0
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series—0
Pix2Streams: Dynamic Hydrology Maps from Satellite-LiDAR Fusion—0
Planning of Fast Charging Infrastructure for Electric Vehicles in a Distribution System and Prediction of Dynamic Price—0
Plotting time: On the usage of CNNs for time series classification—0
p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting—0
Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications—0
POLA: Online Time Series Prediction by Adaptive Learning Rates—0
Policy Choice in Time Series by Empirical Welfare Maximization—0
Policy Gradient Reinforcement Learning for Policy Represented by Fuzzy Rules: Application to Simulations of Speed Control of an Automobile—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs—0
Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data—0
Portfolio Optimization under Fast Mean-reverting and Rough Fractional Stochastic Environment—0
Portfolio Risk Assessment using Copula Models—0
Position-based Content Attention for Time Series Forecasting with Sequence-to-sequence RNNs—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