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 6151–6175 of 6748 papers

TitleStatusHype
Limits to causal inference with state-space reconstruction for infectious disease—0
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories—0
Tool and Phase recognition using contextual CNN features—0
Recurrent switching linear dynamical systemsCode0
Universality of Bayesian mixture predictors—0
Are Chinese transport policies effective? A new perspective from direct pollution rebound effect, and empirical evidence from road transport sector—0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Distributed and parallel time series feature extraction for industrial big data applicationsCode0
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time SeriesCode0
Representation Learning with Deconvolution for Multivariate Time Series Classification and VisualizationCode0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time SeriesCode0
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition—0
Techniques for multifractal spectrum estimation in financial time series—0
Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks—0
Maximally Divergent Intervals for Anomaly Detection—0
Scalable Pooled Time Series of Big Video Data from the Deep Web—0
An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes—0
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity—0
A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade—0
Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model—0
Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalisation—0
Lensless Imaging with Compressive Ultrafast Sensing—0
Similarity Learning for Time Series Classification—0
Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven 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