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 1601–1625 of 6748 papers

TitleStatusHype
Probabilistic Traffic Forecasting with Dynamic RegressionCode0
Enhancing Identification of Structure Function of Academic Articles Using Contextual InformationCode0
Enhancing Time Series Momentum Strategies Using Deep Neural NetworksCode0
Online Search With Best-Price and Query-Based PredictionsCode0
End-to-End Learned Early Classification of Time Series for In-Season Crop Type MappingCode0
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square ErrorCode0
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel DataCode0
On Periodicity Detection and Structural Periodic SimilarityCode0
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time SeriesCode0
End-to-end learning of energy-based representations for irregularly-sampled signals and imagesCode0
Bayesian nonparametric discontinuity designCode0
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time SeriesCode0
ATCN: Resource-Efficient Processing of Time Series on EdgeCode0
Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized ModelsCode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Elastic Product Quantization for Time SeriesCode0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
Option Pricing and Hedging for Discrete Time Autoregressive Hidden Markov ModelCode0
Analysis of Thai Capital Market Linkages: Part I. Bivariate Copula ApproachCode0
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
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Elastic bands across the path: A new framework and methods to lower bound DTWCode0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
Efficient learning of nonlinear prediction models with time-series privileged informationCode0
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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