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 1476–1500 of 6748 papers

TitleStatusHype
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
On Designing Data Models for Energy Feature Stores—0
Policy Choice in Time Series by Empirical Welfare Maximization—0
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction—0
Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data—0
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance—0
Anomaly Detection in Intra-Vehicle Networks—0
Stock Price Prediction Based on Natural Language ProcessingCode0
Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery—0
Summary Markov Models for Event Sequences—0
LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding—0
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Discovering stochastic dynamical equations from biological time series dataCode1
KnitCity: a machine learning-based, game-theoretical framework for prediction assessment and seismic risk policy design—0
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data—0
COVID-19 epidemiology as emergent behavior on a dynamic transmission forestCode0
MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time SeriesCode1
Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting—0
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
A walk through of time series analysis on quantum computers—0
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing DataCode0
Incorporating Stock Market Signals for Twitter Stance DetectionCode0
Neural Machine Translation for Fact-checking Temporal Claims—0
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection—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