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 4176–4200 of 6748 papers

TitleStatusHype
Aortic Pressure Forecasting with Deep Sequence Learning—0
Propagation Graph Estimation from Individual's Time Series of Observed States—0
Process Knowledge Driven Change Point Detection for Automated Calibration of Discrete Event Simulation Models Using Machine Learning—0
Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles—0
Interpretable Deep Representation Learning from Temporal Multi-view Data—0
Revealing hidden dynamics from time-series data by ODENet—0
A Multi-Variate Triple-Regression Forecasting Algorithm for Long-Term Customized Allergy Season Prediction—0
Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso—0
Temporal-Framing Adaptive Network for Heart Sound Segmentation without Prior Knowledge of State Duration—0
Social Media Information Sharing for Natural Disaster Response—0
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors—0
Knowledge Enhanced Neural Fashion Trend ForecastingCode0
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University—0
On a computationally-scalable sparse formulation of the multidimensional and non-stationary maximum entropy principleCode0
Optimizing Temporal Convolutional Network inference on FPGA-based accelerators—0
Approaches and Applications of Early Classification of Time Series: A Review—0
Joint Multi-Dimensional Model for Global and Time-Series Annotations—0
P2ExNet: Patch-based Prototype Explanation Network—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—0
DETECT: A Hierarchical Clustering Algorithm for Behavioural Trends in Temporal Educational Data—0
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GANCode0
Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example—0
Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages—0
Cost-Effective Bad Synchrophasor Data Detection Based on Unsupervised Time Series Data Analytics—0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—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