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 1601–1650 of 6748 papers

TitleStatusHype
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square ErrorCode0
On Mini-Batch Training with Varying Length Time SeriesCode0
Evaluating generation of chaotic time series by convolutional generative adversarial networksCode0
Exoplanet Detection using Machine LearningCode0
eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart ChairCode0
Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with TrendCode0
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel DataCode0
On the separation of shape and temporal patterns in time series -Application to signature authentication-Code0
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for CzechiaCode0
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational CostsCode0
Opinion Prediction with User FingerprintingCode0
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time SeriesCode0
Ensemble transport smoothing. Part I: Unified frameworkCode0
Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation AnalysisCode0
Patch LearningCode0
Ousiometrics and Telegnomics: The essence of meaning conforms to a two-dimensional powerful-weak and dangerous-safe framework with diverse corpora presenting a safety biasCode0
Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noiseCode0
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman FilterCode0
Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized ModelsCode0
Enhancing Time Series Momentum Strategies Using Deep Neural NetworksCode0
EnK: Encoding time-information in convolutionCode0
Ensemble of heterogeneous flexible neural trees using multiobjective genetic programmingCode0
Data augmentation using synthetic data for time series classification with deep residual networksCode0
Data Consistency Approach to Model ValidationCode0
Path Imputation Strategies for Signature Models of Irregular Time SeriesCode0
Path Signature Area-Based Causal Discovery in Coupled Time SeriesCode0
Enhancing Identification of Structure Function of Academic Articles Using Contextual InformationCode0
Bayesian nonparametric discontinuity designCode0
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-SeriesCode0
Data-driven detrending of nonstationary fractal time series with echo state networksCode0
PI-Net: A Deep Learning Approach to Extract Topological Persistence ImagesCode0
Ensemble Sales Forecasting Study in Semiconductor IndustryCode0
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance MatricesCode0
Pooled Motion Features for First-Person VideosCode0
Estimating Vector Fields from Noisy Time SeriesCode0
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time SeriesCode0
End-to-End Learned Early Classification of Time Series for In-Season Crop Type MappingCode0
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium PropagationCode0
Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural NetworksCode0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
Data Augmentation for Generating Synthetic Electrogastrogram Time SeriesCode0
Analyzing Linear Dynamical Systems: From Modeling to Coding and LearningCode0
End-to-end learning of energy-based representations for irregularly-sampled signals and imagesCode0
Elastic Product Quantization for Time SeriesCode0
Elastic bands across the path: A new framework and methods to lower bound DTWCode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
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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