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 1051–1100 of 6748 papers

TitleStatusHype
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation—0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19—0
A new measure between sets of probability distributions with applications to erratic financial behavior—0
Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders—0
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—0
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering—0
Autoregressive Quantile Flows for Predictive Uncertainty Estimation—0
A New Look to Three-Factor Fama-French Regression Model using Sample Innovations—0
Distributional uncertainty of the financial time series measured by G-expectation—0
Autoregressive-Model-Based Methods for Online Time Series Prediction with Missing Values: an Experimental Evaluation—0
A new approach to the modeling of financial volumes—0
Autoregressive GNN-ODE GRU Model for Network Dynamics—0
A new approach for physiological time series—0
Adversarially learned anomaly detection for time series data—0
An Evolving Cascade System Based on A Set Of Neo Fuzzy Nodes—0
A Comprehensive Study on Various Statistical Techniques for Prediction of Movie Success—0
Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations—0
Class-Specific Attention (CSA) for Time-Series Classification—0
Cluster-and-Conquer: A Framework For Time-Series Forecasting—0
An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning—0
Classifying Frames at the Sentence Level in News Articles—0
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves—0
An Evaluation of Classification Methods for 3D Printing Time-Series Data—0
Classifying Contaminated Cell Cultures using Time Series Features—0
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
Automatic time-series phenotyping using massive feature extraction—0
Automatic Synthesis of Neurons for Recurrent Neural Nets—0
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery—0
An Evaluation of Classification and Outlier Detection Algorithms—0
Classifiers With a Reject Option for Early Time-Series Classification—0
Classifying Image Sequences of Astronomical Transients with Deep Neural Networks—0
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning—0
Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone—0
Automatic Registration and Clustering of Time Series—0
A Neural Stochastic Volatility Model—0
Automatic Generation of Student Report Cards—0
Adversarial Domain Adaptation for Stable Brain-Machine Interfaces—0
Automatic Generation of Probabilistic Programming from Time Series Data—0
Automatic Forecasting via Meta-Learning—0
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series—0
Classification with the matrix-variate-t distribution—0
Classifying Pattern and Feature Properties to Get a Θ(n) Checker and Reformulation for Sliding Time-Series Constraints—0
Automatic estimation of heading date of paddy rice using deep learning—0
A Neural Implementation of the Kalman Filter—0
Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality—0
An Euler-based GAN for time series—0
A complex network approach to time series analysis with application in diagnosis of neuromuscular disorders—0
Automatic Detection Of Noise Events at Shooting Range Using Machine Learning—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