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

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Showing 426–450 of 6748 papers

TitleStatusHype
An efficient aggregation method for the symbolic representation of temporal dataCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)Code1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
HARNet: A Convolutional Neural Network for Realized Volatility ForecastingCode1
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image RepresentationCode1
Highly comparative time-series analysis: The empirical structure of time series and their methodsCode1
An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life predictionCode1
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
HIVE-COTE 2.0: a new meta ensemble for time series classificationCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
An Empirical Evaluation of Time-Series Feature SetsCode1
Advancing the State-of-the-Art for ECG Analysis through Structured State Space ModelsCode1
An Empirical Framework for Domain Generalization in Clinical SettingsCode1
HYDRA: Competing convolutional kernels for fast and accurate time series classificationCode1
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical ImagesCode1
Imaging Time-Series to Improve Classification and ImputationCode1
Improving Deep Learning Interpretability by Saliency Guided TrainingCode1
An Empirical Survey of Data Augmentation for Time Series Classification with Neural NetworksCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Improving Position Encoding of Transformers for Multivariate Time Series ClassificationCode1
Can LLMs Understand Time Series Anomalies?Code1
A Bayesian neural network predicts the dissolution of compact planetary systemsCode1
Adversarial Attacks on Time SeriesCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
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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