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

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Showing 4601–4650 of 6748 papers

TitleStatusHype
Bayesian nonparametric discontinuity designCode0
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Modelling EHR timeseries by restricting feature interaction—0
Robust Parameter-Free Season Length Detection in Time SeriesCode0
Synthetic Event Time Series Health Data Generation—0
Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatilityCode0
Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter's State of the Art—0
Self-supervised representation learning from electroencephalography signalsCode0
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning—0
Generating an Explainable ECG Beat Space With Variational Auto-Encoders—0
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural NetworksCode0
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation NetworksCode0
Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge SolutionCode0
Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP EventsCode0
Time2Graph: Revisiting Time Series Modeling with Dynamic ShapeletsCode0
Making Good on LSTMs' Unfulfilled Promise—0
SeismoGen: Seismic Waveform Synthesis Using Generative Adversarial Networks—0
DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems—0
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs—0
XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture ClassificationCode0
Discovering Invariances in Healthcare Neural Networks—0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—0
Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances—0
Architectural Tricks for Deep Learning in Remote Photoplethysmography—0
Deep Learning for Stock Selection Based on High Frequency Price-Volume Data—0
An Information Theory Approach on Deciding Spectroscopic Follow UpsCode0
Dynamic Time Warp Convolutional Networks—0
Deep Hedging: Learning to Simulate Equity Option MarketsCode0
Novel semi-metrics for multivariate change point analysis and anomaly detection—0
Framework for Inferring Following Strategies from Time Series of Movement DataCode0
Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data—0
Seasonally-Adjusted Auto-Regression of Vector Time Series—0
Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis—0
Optimal Transport Based Change Point Detection and Time Series Segment Clustering—0
Generalizing to unseen domains via distribution matchingCode0
DSANet: Dual Self-Attention Network for Multivariate Time Series ForecastingCode0
Variational Bayesian inference of hidden stochastic processes with unknown parameters—0
Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning—0
Decoding of visual-related information from the human EEG using an end-to-end deep learning approach—0
Identifying Predictive Causal Factors from News Streams—0
LFZip: Lossy compression of multivariate floating-point time series data via improved predictionCode0
Research and application of time series algorithms in centralized purchasing data—0
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings—0
Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data—0
Detecting correlations and triangular arbitrage opportunities in the Forex by means of multifractal detrended cross-correlations analysis—0
Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devicesCode0
Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries—0
Convolutional Conditional Neural ProcessesCode0
Harnessing the power of Topological Data Analysis to detect change points in time seriesCode0
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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