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 6401–6425 of 6748 papers

TitleStatusHype
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian ProcessesCode0
Deep Learning for Predicting Asset ReturnsCode0
Metric on Nonlinear Dynamical Systems with Perron-Frobenius OperatorsCode0
MEx: Multi-modal Exercises Dataset for Human Activity RecognitionCode0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
Variationally Inferred Sampling Through a Refined BoundCode0
Explaining Deep Classification of Time-Series Data with Learned PrototypesCode0
MIA-Prognosis: A Deep Learning Framework to Predict Therapy ResponseCode0
micompm: A MATLAB/Octave toolbox for multivariate independent comparison of observationsCode0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained DevicesCode0
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIICode0
Combining datasets to increase the number of samples and improve model fittingCode0
Explainable time series tweaking via irreversible and reversible temporal transformationsCode0
Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic PredictionCode0
MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled TrajectoriesCode0
Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop MappingCode0
Minimax Estimation of Partially-Observed Vector AutoRegressionsCode0
Predicting Solar Flares Using a Long Short-Term Memory NetworkCode0
Co-evolutionary multi-task learning for dynamic time series predictionCode0
Adversarial Attacks on Deep Neural Networks for Time Series ClassificationCode0
CNTS: Cooperative Network for Time SeriesCode0
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)Code0
Mining Novel Multivariate Relationships in Time Series Data Using Correlation NetworksCode0
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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