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 6176–6200 of 6748 papers

TitleStatusHype
Fused-Lasso Regularized Cholesky Factors of Large Nonstationary Covariance Matrices of Longitudinal DataCode0
Fully Neural Network based Model for General Temporal Point ProcessesCode0
Defending Black-box Skeleton-based Human Activity ClassifiersCode0
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart HomesCode0
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence FunctionsCode0
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural NetworksCode0
Type-Driven Automated Learning with LaleCode0
A Wavelet Method for Panel Models with Jump Discontinuities in the ParametersCode0
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate PredictionCode0
Linear Dynamics: Clustering without identificationCode0
Order book model with herd behavior exhibiting long-range memoryCode0
A signature-based machine learning model for bipolar disorder and borderline personality disorderCode0
DeepTFP: Mobile Time Series Data Analytics based Traffic Flow PredictionCode0
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODECode0
Parameter inference from a non-stationary unknown processCode0
Unsupervised real-time anomaly detection for streaming dataCode0
Sampling and Reconstruction of Signals on Product GraphsCode0
Deep Temporal Sigmoid Belief Networks for Sequence ModelingCode0
Structure Discovery in Nonparametric Regression through Compositional Kernel SearchCode0
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer InformationCode0
An Empirical Analysis of how Internet Access Influences Public Opinion towards Undocumented Immigrants and Unaccompanied ChildrenCode0
Sequence Prediction using Spectral RNNsCode0
Forecasting with Multiple SeasonalityCode0
Continual Learning for Human State MonitoringCode0
Structured Self-Attention Weights Encode Semantics in Sentiment AnalysisCode0
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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