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 976–1000 of 6748 papers

TitleStatusHype
Causality and Correlations between BSE and NYSE indexes: A Janus Faced Relationship—0
Bayesian inference and superstatistics to describe long memory processes of financial time series—0
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
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems—0
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
Bayesian nonparametric shared multi-sequence time series segmentation—0
Bayesian nonparametric sparse VAR models—0
Affine and Regional Dynamic Time Warpng—0
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting—0
Bayesian Online Change Point Detection for Baseline Shifts—0
Bayesian Optimisation for a Biologically Inspired Population Neural Network—0
Aedes-AI: Neural Network Models of Mosquito Abundance—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
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
An Explainer for Temporal Graph Neural Networks—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—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