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 1426–1450 of 6748 papers

TitleStatusHype
A data filling methodology for time series based on CNN and (Bi)LSTM neural networks—0
Cyclocopula Technique to Study the Relationship Between Two Cyclostationary Time Series with Fractional Brownian Motion Errors—0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Configuration and Collection Factors for Side-Channel Disassembly—0
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation—0
Confident Kernel Sparse Coding and Dictionary Learning—0
Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network—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
A Scrambled Method of Moments—0
A Self-Supervised Framework for Function Learning and Extrapolation—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
Conformal Prediction with Temporal Quantile Adjustments—0
Confidence-Guided Learning Process for Continuous Classification of Time Series—0
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data—0
Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction—0
A Data-Driven Method for Recognizing Automated Negotiation Strategies—0
Current state of nonlinear-type time-frequency analysis and applications to high-frequency biomedical signals—0
A Cellular Automaton Model for the generation of Brainwaves—0
A multifactorial evaluation framework for gene regulatory network reconstruction—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
ASAT: Adaptively Scaled Adversarial Training in 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