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 4376–4400 of 6748 papers

TitleStatusHype
Instantaneous Modelling and Reverse Engineering of DataConsistent Prime Models in Seconds!—0
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education—0
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware—0
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods—0
Integrated information and dimensionality in continuous attractor dynamics—0
Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining—0
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions—0
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction—0
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis—0
Interdependency between the Stock Market and Financial News—0
Interpolation and Gap Filling of Landsat Reflectance Time Series—0
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series—0
Interpretable Categorization of Heterogeneous Time Series Data—0
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques—0
Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
Interpretable Feature Construction for Time Series Extrinsic Regression—0
Interpretable Feature Engineering for Time Series Predictors using Attention Networks—0
Interpretable Latent Variables in Deep State Space Models—0
Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications—0
Interpretable Models for Understanding Immersive Simulations—0
Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks—0
Interpretable Neural Networks for Panel Data Analysis in Economics—0
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories—0
Interpretable Super-Resolution via a Learned Time-Series Representation—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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