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 2101–2150 of 6748 papers

TitleStatusHype
Learning Graph Neural Networks for Multivariate Time Series Anomaly DetectionCode1
Machine Learning for Genomic Data—0
On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series—0
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
Forecasting Crude Oil Price Using Event Extraction—0
Decoding Causality by Fictitious VAR Modeling—0
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts—0
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes—0
Nyström Regularization for Time Series Forecasting—0
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting—0
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection—0
Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned—0
Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data—0
GraSSNet: Graph Soft Sensing Neural Networks—0
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series—0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks—0
Soft Sensing Transformer: Hundreds of Sensors are Worth a Single WordCode0
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting—0
Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility informationCode0
American Hate Crime Trends Prediction with Event Extraction—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
A toolkit for data-driven discovery of governing equations in high-noise regimesCode0
Mimic: An adaptive algorithm for multivariate time series classification—0
A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters—0
Stock Portfolio Optimization Using a Deep Learning LSTM Model—0
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling—0
CoughTrigger: Earbuds IMU Based Cough Detection Activator Using An Energy-efficient Sensitivity-prioritized Time Series Classifier—0
DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting—0
Meta-Forecasting by combining Global Deep Representations with Local Adaptation—0
Transferable Time-Series Forecasting under Causal Conditional ShiftCode1
Dynamic Data Augmentation with Gating Networks for Time Series RecognitionCode1
Coherent Probabilistic Aggregate Queries on Long-horizon ForecastsCode1
LibCity: An Open Library for Traffic PredictionCode2
Unsupervised Change Detection of Extreme Events Using ML On-BoardCode1
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift EstimationCode1
Efficacy the of Confinement Policies on the COVID-19 Spread Dynamics in the Early Period of the Pandemic—0
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time SeriesCode0
Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations—0
Roadmap on Signal Processing for Next Generation Measurement Systems—0
Predictive Auto-scaling with OpenStack MonascaCode0
Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice—0
Deep Learning Algorithms for Hedging with FrictionsCode0
Quality change: norm or exception? Measurement, Analysis and Detection of Quality Change in WikipediaCode0
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks—0
Time Series Comparisons in Deep Space Network—0
Metaphor Development in Public Discourse Using an ARIMA Time Series Analysis Approach—0
Truth-Conditional Captions for Time Series DataCode1
RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual DataCode1
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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