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 1451–1500 of 6748 papers

TitleStatusHype
Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model—0
Construction of neural networks for realization of localized deep learning—0
Data-driven soiling detection in PV modules—0
Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural Network—0
A Cellular Automaton Model for the generation of Brainwaves—0
ASAT: Adaptively Scaled Adversarial Training in Time Series—0
Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community—0
Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context—0
Context-Aware Ensemble Learning for Time Series—0
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions—0
A Signal Detection Scheme Based on Deep Learning in OFDM Systems—0
Context Dependent Semantic Parsing over Temporally Structured Data—0
Context-invariant, multi-variate time series representations—0
A Multi-modal Deep Learning Model for Video Thumbnail Selection—0
A similarity measurement for time series and its application to the stock market—0
A cloud-IoT platform for passive radio sensing: challenges and application case studies—0
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter—0
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions—0
Continual Learning Using Bayesian Neural Networks—0
Data-driven Residual Generation for Early Fault Detection with Limited Data—0
Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining—0
Continuous Convolutional Neural Network forNonuniform Time Series—0
Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE—0
A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads—0
Data-Driven Time Series Reconstruction for Modern Power Systems Research—0
A Soft Computing Approach for Selecting and Combining Spectral Bands—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Among-site variability in the stochastic dynamics of East African coral reefs—0
Artificial neural network as a universal model of nonlinear dynamical systems—0
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces—0
A Data-driven Market Simulator for Small Data Environments—0
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Conditional Risk Minimization for Stochastic Processes—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Kernel Hypothesis Testing with Set-valued Data—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—0
Controlled time series generation for automotive software-in-the-loop testing using GANs—0
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels—0
Controlling False Discovery Rates under Cross-Sectional Correlations—0
Convergence of GANs Training: A Game and Stochastic Control Methodology—0
Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model—0
A Robust Score-Driven Filter for Multivariate 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