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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5801–5850 of 6748 papers

TitleStatusHype
Complexity and Persistence of Price Time Series of the European Electricity Spot Market—0
Complexity-based Financial Stress Evaluation—0
Complexity Measures and Features for Times Series classification—0
Complex market dynamics in the light of random matrix theory—0
Complex systems approach to natural language—0
Complex systems: features, similarity and connectivity—0
Complex-valued Gaussian Process Regression for Time Series Analysis—0
Composable Generative Models—0
Composite FORCE learning of chaotic echo state networks for time-series prediction—0
Composition Properties of Inferential Privacy for Time-Series Data—0
Comprehensive Review of Neural Differential Equations for Time Series Analysis—0
Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases—0
Computer activity learning from system call time series—0
Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression—0
Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets—0
Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis—0
Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation—0
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution—0
Conditional Generative Models for Counterfactual Explanations—0
Conditional heteroskedasticity in crypto-asset returns—0
Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Conditional Risk Minimization for Stochastic Processes—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
Confidence-Guided Learning Process for Continuous Classification of Time Series—0
Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network—0
Confident Kernel Sparse Coding and Dictionary Learning—0
Configuration and Collection Factors for Side-Channel Disassembly—0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Conformalized density- and distance-based anomaly detection in time-series data—0
Conformal k-NN Anomaly Detector for Univariate Data Streams—0
Conformal Prediction Bands for Two-Dimensional Functional Time Series—0
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets—0
Conformal Prediction with Temporal Quantile Adjustments—0
Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction—0
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints—0
Constraints on parameter choices for successful reservoir computing—0
Constructing Time Series Shape Association Measures: Minkowski Distance and Data Standardization—0
Construction Cost Index Forecasting: A Multi-feature Fusion Approach—0
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
Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural Network—0
Containment strategies and statistical measures for the control of Bovine Viral Diarrhea spread in livestock trade networks—0
Contemporary machine learning: a guide for practitioners in the physical sciences—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
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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