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

Showing 63516400 of 6748 papers

TitleStatusHype
Deep Markov Spatio-Temporal FactorizationCode0
Applying Machine Learning to Crowd-sourced Data from Earthquake DetectiveCode0
Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTWCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Complex Gated Recurrent Neural NetworksCode0
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent KernelsCode0
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series ClassifiersCode0
Time series anomaly detection with reconstruction-based state-space modelsCode0
Semantic Discord: Finding Unusual Local Patterns for Time SeriesCode0
Fast and Robust Online Inference with Stochastic Gradient Descent via Random ScalingCode0
Precision and Recall for Time SeriesCode0
Autonomous Deep Quality Monitoring in Streaming EnvironmentsCode0
Data-driven approach in a compartmental epidemic model to assess undocumented infectionsCode0
Apply Artificial Neural Network to Solving Manpower Scheduling ProblemCode0
Synthesis of Parametric Hybrid Automata from Time SeriesCode0
Fast and Accurate Time Series Classification with WEASELCode0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries ChallengeCode0
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural NetworksCode0
Variational online learning of neural dynamicsCode0
Memory-Efficient Centroid Decomposition for Long Time SeriesCode0
Fairness in Forecasting of Observations of Linear Dynamical SystemsCode0
Memory-Gated Recurrent NetworksCode0
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal DynamicsCode0
Adaptive Multi-Agent Continuous Learning SystemCode0
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial NetworksCode0
Variationally Inferred Sampling Through a Refined Bound for Probabilistic ProgramsCode0
Predicting Customer Churn: Extreme Gradient Boosting with Temporal DataCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Factor-Driven Two-Regime RegressionCode0
Multi-task Meta Label Correction for Time Series PredictionCode0
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial FibrillationCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta LearningCode0
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium PropagationCode0
Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep LearningCode0
Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen PredictionCode0
Factor-augmented tree ensemblesCode0
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory PredictionCode0
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluationCode0
Deep-Learnt Classification of Light CurvesCode0
Meteorological indicators of dengue epidemics in non-endemic Northwest ArgentinaCode0
Semi-unsupervised Learning for Time Series ClassificationCode0
Deep Learning MacroeconomicsCode0
Semi-unsupervised Learning of Human Activity using Deep Generative ModelsCode0
Predicting Landsat Reflectance with Deep Generative FusionCode0
Extracting Relationships by Multi-Domain MatchingCode0
Comparing Temporal Graphs Using Dynamic Time WarpingCode0
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease ProgressionCode0
Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified