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 1326–1350 of 6748 papers

TitleStatusHype
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study—0
A Stochastic Time Series Model for Predicting Financial Trends using NLP—0
A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread—0
An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves—0
A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms—0
A Bayesian approach for structure learning in oscillating regulatory networks—0
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification—0
Correcting motion induced fluorescence artifacts in two-channel neural imaging—0
Cyclical Electromechanical Error Denial System Using Matrix Profile—0
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process—0
An anomaly prediction framework for financial IT systems using hybrid machine learning methods—0
A statistical test of market efficiency based on information theory—0
An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation—0
A Deep Learning Approach to Detect Lean Blowout in Combustion Systems—0
A Statistical Machine Learning Approach to Yield Curve Forecasting—0
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems—0
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions—0
Non-stationary GARCH modelling for fitting higher order moments of financial series within moving time windows—0
A State-Space Approach to Dynamic Nonnegative Matrix Factorization—0
A CNN–LSTM model for gold price time-series forecasting—0
Convolutional Sequence Modeling Revisited—0
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements—0
Asset volatility forecasting:The optimal decay parameter in the EWMA model—0
An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments—0
Asset Pricing with General Transaction Costs: Theory and Numerics—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