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 1051–1075 of 6748 papers

TitleStatusHype
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Adversarial Attacks on Deep Neural Networks for Time Series ClassificationCode0
Human Activity Recognition using Multi-Head CNN followed by LSTMCode0
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processesCode0
Homological Time Series Analysis of Sensor Signals from Power PlantsCode0
Automated Deep Abstractions for Stochastic Chemical Reaction NetworksCode0
Automated data-driven approach for gap filling in the time series using evolutionary learningCode0
Adversarial Attacks on Deep Models for Financial Transaction RecordsCode0
Semi-supervised Sequence Modeling for Elastic Impedance InversionCode0
AutoFITS: Automatic Feature Engineering for Irregular Time SeriesCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
High dimensional regression for regenerative time-series: an application to road traffic modelingCode0
High-dimensional regression with potential prior information on variable importanceCode0
Autoencoder-based Representation Learning from Heterogeneous Multivariate Time Series Data of Mechatronic SystemsCode0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOpsCode0
Hide-and-Seek Privacy ChallengeCode0
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different DimensionsCode0
Hierarchical Attention-Based Recurrent Highway Networks for Time Series PredictionCode0
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesCode0
Highly Scalable and Provably Accurate Classification in Poincare BallsCode0
A user-driven case-based reasoning tool for infilling missing values in daily mean river flow recordsCode0
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk PredictionCode0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mappingCode0
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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