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 4801–4825 of 6748 papers

TitleStatusHype
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer—0
Metric Learning for Temporal Sequence Alignment—0
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series—0
MIDAS: Deep learning human action intention prediction from natural eye movement patterns—0
Mimic: An adaptive algorithm for multivariate time series classification—0
Min(d)ing the President: A text analytic approach to measuring tax news—0
Mind Your POV: Convergence of Articles and Editors Towards Wikipedia's Neutrality Norm—0
Minimax Concave Penalty Regularized Adaptive System Identification—0
Minimax Time Series Prediction—0
Mining and modeling complex leadership-followership dynamics of movement data—0
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting—0
Mining Illegal Insider Trading of Stocks: A Proactive Approach—0
Mining linguistic tone patterns with symbolic representation—0
Social Media Information Sharing for Natural Disaster Response—0
Mining Sub-Interval Relationships In Time Series Data—0
Mining the Mind: Linear Discriminant Analysis of MEG source reconstruction time series supports dynamic changes in deep brain regions during meditation sessions—0
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction—0
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes—0
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space—0
Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation—0
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data—0
Mixed Membership Models for Time Series—0
Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series—0
Mixed pooling of seasonality for time series forecasting: An application to pallet transport data—0
Mixing Times and Structural Inference for Bernoulli Autoregressive Processes—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