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 4676–4700 of 6748 papers

TitleStatusHype
Longitudinal Support Vector Machines for High Dimensional Time Series—0
Longitudinal Variational Autoencoder—0
Long-Range Correlation Underlying Childhood Language and Generative Models—0
Long-range memory and multifractality in gold markets—0
Long-run dynamics of the U.S. patent classification system—0
Long Run Risk in Stationary Structural Vector Autoregressive Models—0
Long Short-Term Memory Neural Network for Financial Time Series—0
Long Short-term Memory RNN—0
Long-term hail risk assessment with deep neural networks—0
Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networks—0
Long-Term Online Smoothing Prediction Using Expert Advice—0
Long-term Prediction of Nonlinear Time Series Using Autoencoder and Echo State Networks—0
Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion—0
Long vs Short Time Scales: the Rough Dilemma and Beyond—0
Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches—0
Loss-analysis via Attention-scale for Physiologic Time Series—0
Loss meta-learning for forecasting—0
Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection—0
Lost in Time: Temporal Analytics for Long-Term Video Surveillance—0
Low-dimensional Models in Spatio-Temporal Wind Speed Forecasting—0
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly Likelihood—0
Low-pass filtering as Bayesian inference—0
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation—0
Low Rank Forecasting—0
Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting—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