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 1401–1425 of 6748 papers

TitleStatusHype
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis—0
A cloud-IoT platform for passive radio sensing: challenges and application case studies—0
A Signal Detection Scheme Based on Deep Learning in OFDM Systems—0
A Short Survey of Topological Data Analysis in Time Series and Systems Analysis—0
A Multi-Modal and Multitask Benchmark in the Clinical Domain—0
A Short Image Series Based Scheme for Time Series Digital Image Correlation—0
A Shapelet Transform for Multivariate Time Series Classification—0
A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture—0
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data—0
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring—0
A Sequential Modelling Approach for Indoor Temperature Prediction and Heating Control in Smart Buildings—0
Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reports—0
A sequential approach to calibrate ecosystem models with multiple time series data—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
A data-informed mathematical model of microglial cell dynamics during ischemic stroke in the middle cerebral artery—0
A Basic Recurrent Neural Network Model—0
A multifactorial evaluation framework for gene regulatory network reconstruction—0
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data—0
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping—0
A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade—0
A data imputation method for multivariate time series based on generative adversarial network—0
Cross-Modal Data Programming Enables Rapid Medical Machine Learning—0
Cross-Modal Virtual Sensing for Combustion Instability Monitoring—0
A Self-Supervised Framework for Function Learning and Extrapolation—0
A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls—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