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 3501–3550 of 6748 papers

TitleStatusHype
Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning—0
Physics-aware, probabilistic model order reduction with guaranteed stability—0
Unveiling the role of plasticity rules in reservoir computing—0
Deep State Inference: Toward Behavioral Model Inference of Black-box Software SystemsCode0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—0
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling—0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information—0
Reliable Fleet Analytics for Edge IoT Solutions—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning ApproachCode0
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation—0
Challenges and approaches to time-series forecasting in data center telemetry: A Survey—0
General Hannan and Quinn Criterion for Common Time Series—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
Bootstrapping Non-Stationary Stochastic Volatility—0
Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform—0
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data—0
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection—0
Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models—0
The data synergy effects of time-series deep learning models in hydrology—0
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI—0
Data-Driven Copy-Paste Imputation for Energy Time SeriesCode0
A Trainable Reconciliation Method for Hierarchical Time-Series—0
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph—0
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data—0
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks—0
Parkinson's Disease Diagnosis Using Deep Learning—0
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network—0
Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19—0
Silicon Photonic Microring Based Chip-Scale Accelerator for Delayed Feedback Reservoir Computing—0
Leveraging latent persistency in United States patent and trademark applications to gain insight into the evolution of an innovation-driven economy—0
Latent Space Semi-Supervised Time Series Data Clustering—0
The 2020 Global Stock Market Crash: Endogenous or Exogenous?—0
Anomaly detection and regime searching in fitness-tracker data—0
A Multi-Modal and Multitask Benchmark in the Clinical Domain—0
Jumpy Recurrent Neural Networks—0
Anomaly detection in dynamical systems from measured time series—0
Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion—0
Recurrent Neural Network Architecture based on Dynamic Systems Theory for Data Driven Modelling of Complex Physical Systems—0
Detecting residues of cosmic events using residual neural network—0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data—0
Latent Convergent Cross Mapping—0
Generative Time-series Modeling with Fourier Flows—0
Graph Edit NetworksCode0
"Hey, that's not an ODE'": Faster ODE Adjoints with 12 Lines of Code—0
An Euler-based GAN for time series—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