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 1801–1850 of 6748 papers

TitleStatusHype
A Method for Massively Parallel Analysis of Time Series—0
Deep Learning for Satellite Image Time Series Analysis: A Review—0
Deep Learning for Stock Selection Based on High Frequency Price-Volume Data—0
Deep learning for structural health monitoring: An application to heritage structures—0
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies—0
Deep Learning for Time-Series Analysis—0
Composable Generative Models—0
Complex-valued Gaussian Process Regression for Time Series Analysis—0
A review on distance based time series classification—0
A Data-Driven Approach for Modeling Stochasticity in Oil Market—0
Deep Learning in Asset Pricing—0
Deep Learning in Multiple Multistep Time Series Prediction—0
Deep-Learning Inversion of Seismic Data—0
Automatic Detection Of Noise Events at Shooting Range Using Machine Learning—0
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells—0
Complex systems: features, similarity and connectivity—0
Deep Learning, Predictability, and Optimal Portfolio Returns—0
Complex systems approach to natural language—0
Deep Learning to Attend to Risk in ICU—0
On model selection for scalable time series forecasting in transport networks—0
A Review on Deep Learning in UAV Remote Sensing—0
Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery—0
Deep Learning with Kernel Flow Regularization for Time Series Forecasting—0
Deep Learning with Long Short-Term Memory for Time Series Prediction—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
Complex market dynamics in the light of random matrix theory—0
DeepMoTIon: Learning to Navigate Like Humans—0
Deep MR Fingerprinting with total-variation and low-rank subspace priors—0
Deep Multimodal Learning: An Effective Method for Video Classification—0
Complexity Measures and Features for Times Series classification—0
Deep multi-survey classification of variable stars—0
A Review of Wind Speed and Wind Power Forecasting Techniques—0
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings—0
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks—0
Deep Neural Models of Semantic Shift—0
Deep Neural Networks and Neuro-Fuzzy Networks for Intellectual Analysis of Economic Systems—0
Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL—0
Complexity-based Financial Stress Evaluation—0
VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading—0
Deep Neural Networks on EEG signals to predict Attention Score using Gramian Angular Difference Field—0
Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field—0
Complexity and Persistence of Price Time Series of the European Electricity Spot Market—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
A review of two decades of correlations, hierarchies, networks and clustering in financial markets—0
Automatic Synthesis of Neurons for Recurrent Neural Nets—0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice—0
Compensatory model for quantile estimation and application to VaR—0
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties—0
Accurate Prediction of Global Mean Temperature through Data Transformation Techniques—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