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 1101–1125 of 6748 papers

TitleStatusHype
Automatic Detection of Major Freeway Congestion Events Using Wireless Traffic Sensor Data: A Machine Learning Approach—0
A network-based transfer learning approach to improve sales forecasting of new products—0
Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data—0
Automatic deep learning for trend prediction in time series data—0
An Estimation of Online Video User Engagement from Features of Continuous Emotions—0
Quantile Convolutional Neural Networks for Value at Risk Forecasting—0
Automatic Construction of a Recurrent Neural Network based Classifier for Vehicle Passage Detection—0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
An Equilibrium Model for the Cross-Section of Liquidity Premia—0
Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate—0
Clustering Discrete-Valued Time Series—0
Inferring Global Dynamics of a Black-Box System Using Machine Learning—0
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures—0
Automated Testing of AI Models—0
An Ensemble method for Content Selection for Data-to-text Systems—0
Clustering Activity-Travel Behavior Time Series using Topological Data Analysis—0
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration—0
Clustering evolving data using kernel-based methods—0
Clustering of Time Series Data with Prior Geographical Information—0
Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning—0
Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems—0
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks—0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
An End-to-End Model for Time Series Classification In the Presence of Missing Values—0
Automated Model Selection for Time-Series Anomaly Detection—0
Show:102550
← PrevPage 45 of 270Next →
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