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 3051–3075 of 6748 papers

TitleStatusHype
Distributed Online Learning with Multiple Kernels—0
Deep Video Prediction for Time Series Forecasting—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Similarity measure for sparse time course data based on Gaussian processesCode0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition—0
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions—0
Non-stationary GARCH modelling for fitting higher order moments of financial series within moving time windows—0
Optimal Prediction Intervals for Macroeconomic Time Series Using Chaos and NSGA II—0
The SpaceNet Multi-Temporal Urban Development Challenge—0
When is Early Classification of Time Series Meaningful?—0
Model-Attentive Ensemble Learning for Sequence Modeling—0
Bridging factor and sparse models—0
Neural Pharmacodynamic State Space ModelingCode0
Phase Space Reconstruction Network for Lane Intrusion Action Recognition—0
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
nTreeClus: a Tree-based Sequence Encoder for Clustering Categorical SeriesCode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Approximate Bayes factors for unit root testing—0
Joint Characterization of Multiscale Information in High Dimensional Data—0
Composable Generative Models—0
Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks—0
Performance Dependency of LSTM and NAR Beamformers With Respect to Sensor Array Properties in V2I Scenario—0
Analysis of EEG data using complex geometric structurization—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