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 4076–4100 of 6748 papers

TitleStatusHype
Uncertainty Measurement of Basic Probability Assignment Integrity Based on Approximate Entropy in Evidence Theory—0
Uncertainty Modelling in Risk-averse Supply Chain Systems Using Multi-objective Pareto Optimization—0
Uncertainty Quantification for Traffic Forecasting: A Unified Approach—0
Uncertainty, volatility and the persistence norms of financial time series—0
Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos—0
Uncovering differential equations from data with hidden variables—0
Uncovering Feature Interdependencies in High-Noise Environments with Stepwise Lookahead Decision Forests—0
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data—0
Uncovering the Dynamics of Correlation Structures Relative to the Collective Market Motion—0
Uncovering the evolution of non-stationary stochastic variables: the example of asset volume-price fluctuations—0
Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling—0
Understanding Different Design Choices in Training Large Time Series Models—0
Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis—0
Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Understanding the input-output relationship of neural networks in the time series forecasting radon levels at Canfranc Underground Laboratory—0
Understanding the merging behavior patterns and evolutionary mechanism at freeway on-ramps—0
Underwater dual-magnification imaging for automated lake plankton monitoring—0
Undetectable GPS-Spoofing Attack on Time Series Phasor Measurement Unit Data—0
Unfolding recurrence by Green's functions for optimized reservoir computing—0
Unfolding recurrence by Green’s functions for optimized reservoir computing—0
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models—0
Unified recurrent network for many feature types—0
Unified recurrent neural network for many feature types—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