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 2026–2050 of 6748 papers

TitleStatusHype
KDCTime: Knowledge Distillation with Calibration on InceptionTime for Time-series Classification—0
STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting—0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization—0
A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis—0
In situ process quality monitoring and defect detection for direct metal laser melting—0
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty—0
Combining Embeddings and Fuzzy Time Series for High-Dimensional Time Series Forecasting in Internet of Energy Applications—0
Causal-based Time Series Domain Generalization for Vehicle Intention Prediction—0
Complexity and Persistence of Price Time Series of the European Electricity Spot Market—0
Online Search With Best-Price and Query-Based PredictionsCode0
Hydroclimatic time series features at multiple time scales—0
Time-Series Estimation from Randomly Time-Warped Observations—0
Data-Driven Interaction Analysis of Line Failure Cascading in Power Grid Networks—0
A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success—0
Simulation platform for pattern recognition based on reservoir computing with memristor networksCode1
Convergence of GANs Training: A Game and Stochastic Control Methodology—0
A Daily Tourism Demand Prediction Framework Based on Multi-head Attention CNN: The Case of The Foreign Entrant in South Korea—0
Probabilistic Forecasting: A Level-Set Approach—0
Conformal Time-series ForecastingCode1
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Probabilistic Transformer For Time Series Analysis—0
Change Point Detection via Multivariate Singular Spectrum Analysis—0
SurvODE: Extrapolating Gene Expression Distribution for Early Cancer Identification—0
Changepoint Analysis of Topic Proportions in Temporal Text Data—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