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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

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Showing 5501–5550 of 6748 papers

TitleStatusHype
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model—0
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization—0
Bayesian inference of natural selection from allele frequency time series—0
Bayesian LSTMs in medicine—0
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings—0
Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series—0
Bayesian nonparametric shared multi-sequence time series segmentation—0
Bayesian nonparametric sparse VAR models—0
Bayesian Online Change Point Detection for Baseline Shifts—0
Bayesian Optimisation for a Biologically Inspired Population Neural Network—0
Bayesian prediction of jumps in large panels of time series data—0
Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution—0
Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series—0
Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data—0
Behave-XAI: Deep Explainable Learning of Behavioral Representational Data—0
Behavioural Analytics: Mathematics of the Mind—0
Benchmarking adversarial attacks and defenses for time-series data—0
Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series—0
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data—0
Benchmarking Multivariate Time Series Classification Algorithms—0
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens—0
Benchmarking Twitter Sentiment Analysis Tools—0
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series—0
Benefits of Depth for Long-Term Memory of Recurrent Networks—0
Benign Overfitting in Time Series Linear Models with Over-Parameterization—0
Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences—0
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion—0
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data—0
Beyond Predictions in Neural ODEs: Identification and Interventions—0
Beyond S-curves: Recurrent Neural Networks for Technology Forecasting—0
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition—0
Bias, Consistency, and Partisanship in U.S. Asylum Cases: A Machine Learning Analysis of Extraneous Factors in Immigration Court Decisions—0
Bidirectional learning for time-series models with hidden units—0
Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series—0
Bidirectional Recurrent Neural Networks as Generative Models—0
Bid Prediction in Repeated Auctions with Learning—0
Bifurcation Analysis using Zigzag Persistence—0
Bifurcation Spiking Neural Network—0
Bi-LSTM Price Prediction based on Attention Mechanism—0
Bioeconomic analysis of harvesting within a predator-prey system: A case study in the Chesapeake Bay fisheries—0
Biological neurons act as generalization filters in reservoir computing—0
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks—0
Bitcoin Forecasting Using ARIMA and PROPHET—0
Bitcoin Price Predictive Modeling Using Expert Correction—0
Bitcoin Volatility and Intrinsic Time Using Double Subordinated Levy Processes—0
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information—0
Black-Box Autoregressive Density Estimation for State-Space Models—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