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 3651–3700 of 6748 papers

TitleStatusHype
Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State—0
Viking: Variational Bayesian Variance Tracking—0
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems—0
Recursive input and state estimation: A general framework for learning from time series with missing data—0
Recursive Least Squares Policy Control with Echo State Network—0
Recursive Sparse Point Process Regression with Application to Spectrotemporal Receptive Field Plasticity Analysis—0
Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones—0
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Reducing Artificial Neural Network Complexity: A Case Study on Exoplanet Detection—0
Reducing overestimating and underestimating volatility via the augmented blending-ARCH model—0
Reducing statistical time-series problems to binary classification—0
Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand—0
Regional and temporal characteristics of bovine tuberculosis of cattle in Great Britain—0
PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting—0
Regression with Uncertainty Quantification in Large Scale Complex Data—0
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data—0
Regularized Dynamic Boltzmann Machine with Delay Pruning for Unsupervised Learning of Temporal Sequences—0
Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations—0
Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso—0
Regularized Flexible Activation Function Combinations for Deep Neural Networks—0
Regular Time-series Generation using SGM—0
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach—0
Reinforcement Learning Based Dynamic Model Combination for Time Series Forecasting—0
Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting—0
Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation—0
Reinforcement Learning with Convolutional Reservoir Computing—0
Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect—0
Relational Multi-Instance Learning for Concept Annotation from Medical Time Series—0
Relational State-Space Model for Stochastic Multi-Object Systems—0
Reliable Fleet Analytics for Edge IoT Solutions—0
Remaining Useful Life Estimation Using Functional Data Analysis—0
Remaining Useful Lifetime Prediction via Deep Domain Adaptation—0
Remote atrial fibrillation burden estimation using deep recurrent neural network—0
Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones—0
ReNN: Rule-embedded Neural Networks—0
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
Replica approach to mean-variance portfolio optimization—0
Representation Learning by Reconstructing Neighborhoods—0
Representation learning of rare temporal conditions for travel time prediction—0
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series—0
Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers—0
Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models—0
Research and application of time series algorithms in centralized purchasing data—0
Research of an optimization model for servicing a network of ATMs and information payment terminals—0
Breaking Symmetries of the Reservoir Equations in Echo State Networks—0
Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept—0
Reservoir Computing on the Hypersphere—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