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 5451–5500 of 6748 papers

TitleStatusHype
Automatic Detection of Major Freeway Congestion Events Using Wireless Traffic Sensor Data: A Machine Learning Approach—0
Automatic Detection Of Noise Events at Shooting Range Using Machine Learning—0
Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality—0
Automatic estimation of heading date of paddy rice using deep learning—0
Automatic Forecasting via Meta-Learning—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Automatic Generation of Student Report Cards—0
Automatic Registration and Clustering of Time Series—0
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone—0
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning—0
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery—0
Automatic Synthesis of Neurons for Recurrent Neural Nets—0
Automatic time-series phenotyping using massive feature extraction—0
Autoregressive GNN-ODE GRU Model for Network Dynamics—0
Autoregressive-Model-Based Methods for Online Time Series Prediction with Missing Values: an Experimental Evaluation—0
Distributional uncertainty of the financial time series measured by G-expectation—0
Autoregressive Quantile Flows for Predictive Uncertainty Estimation—0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning—0
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders—0
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation—0
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors—0
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network—0
A Visibility Graph Averaging Aggregation Operator—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse—0
A walk through of time series analysis on quantum computers—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
A wavelet analysis of inter-dependence, contagion and long memory among global equity markets—0
A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction—0
A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction—0
A windowed correlation based feature selection method to improve time series prediction of dengue fever cases—0
A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—0
Backpropagation-Free Learning Method for Correlated Fuzzy Neural Networks—0
Backpropagation on Dynamical Networks—0
Bag of Recurrence Patterns Representation for Time-Series Classification—0
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition—0
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—0
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?—0
Impulse data models for the inverse problem of electrocardiography—0
Bayesian Alignments of Warped Multi-Output Gaussian Processes—0
Bayesian autoregressive spectral estimation—0
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets—0
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior—0
Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Bayesian inference and superstatistics to describe long memory processes of financial time series—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