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 1001–1025 of 6748 papers

TitleStatusHype
A Formally Robust Time Series Distance Metric—0
Behavioural Analytics: Mathematics of the Mind—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
An Explainer for Temporal Graph Neural Networks—0
Benchmarking adversarial attacks and defenses for time-series data—0
CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs—0
A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property—0
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data—0
Benchmarking Multivariate Time Series Classification Algorithms—0
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens—0
An L0-Norm Constrained Non-Negative Matrix Factorization Algorithm for the Simultaneous Disaggregation of Fixed and Shiftable Loads—0
A framework for anomaly detection using language modeling, and its applications to finance—0
A windowed correlation based feature selection method to improve time series prediction of dengue fever cases—0
An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation—0
A dynamic conditional approach to portfolio weights forecasting—0
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series—0
Benefits of Depth for Long-Term Memory of Recurrent Networks—0
Benign Overfitting in Time Series Linear Models with Over-Parameterization—0
Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences—0
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion—0
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data—0
Beyond Predictions in Neural ODEs: Identification and Interventions—0
An Experimental Evaluation of Nearest Neighbour Time Series Classification—0
A consistent deterministic regression tree for non-parametric prediction of time series—0
CDPS: Constrained DTW-Preserving Shapelets—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