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 2101–2125 of 6748 papers

TitleStatusHype
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection—0
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series—0
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting—0
Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis—0
Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection—0
Detecting Concrete Abnormality Using Time-series Thermal Imaging and Supervised Learning—0
Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags—0
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior—0
An Improved Mathematical Model of Sepsis: Modeling, Bifurcation Analysis, and Optimal Control Study for Complex Nonlinear Infectious Disease System—0
Detecting changes in slope with an L_0 penalty—0
Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization—0
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets—0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Bayesian autoregressive spectral estimation—0
An improved LogNNet classifier for IoT application—0
A fast noise filtering algorithm for time series prediction using recurrent neural networks—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Detecting a trend change in cross-border epidemic transmission—0
Bayesian Alignments of Warped Multi-Output Gaussian Processes—0
Detecting and modelling delayed density-dependence in abundance time series of a small mammal (Didelphis aurita)—0
Impulse data models for the inverse problem of electrocardiography—0
Animal behavior classification via deep learning on embedded systems—0
Locating line and node disturbances in networks of diffusively coupled dynamical agents—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