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 4951–4975 of 6748 papers

TitleStatusHype
Multi-modal Predictive Models of Diabetes Progression—0
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification—0
Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products—0
Multinomial Sampling for Hierarchical Change-Point Detection—0
Multi-Objective Model Selection for Time Series Forecasting—0
Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference—0
Multiple Change Point Estimation in Stationary Ergodic Time Series—0
Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice—0
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers—0
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices—0
Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series—0
Multiple Output Regression with Latent Noise—0
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting—0
Multiple Regularizations Deep Learning for Paddy Growth Stages Classification from LANDSAT-8—0
Multiple shooting with neural differential equations—0
Multiple Time Series Ising Model for Financial Market Simulations—0
Multiplex visibility graphs to investigate recurrent neural networks dynamics—0
Multiplicative Error Models: 20 years on—0
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains—0
Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)—0
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors—0
Multi-scale Attention Flow for Probabilistic Time Series Forecasting—0
Multiscale Causal Structure Learning—0
Multiscale characteristics of the emerging global cryptocurrency market—0
Multiscale Comparison of Nonparametric Trend Curves—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