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 2676–2700 of 6748 papers

TitleStatusHype
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
The elastic origins of tail asymmetry—0
SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging—0
Probabilistic Time Series Forecasts with Autoregressive Transformation Models—0
Semimartingale and continuous-time Markov chain approximation for rough stochastic local volatility models—0
Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction—0
On Adversarial Vulnerability of PHM algorithms: An Initial Study—0
IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance—0
Time Series Clustering for Human Behavior Pattern Mining—0
A Two-layer Approach for Estimating Behind-the-Meter PV Generation Using Smart Meter Data—0
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data—0
Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2—0
Detecting Slag Formations with Deep Convolutional Neural Networks—0
Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals—0
Ousiometrics and Telegnomics: The essence of meaning conforms to a two-dimensional powerful-weak and dangerous-safe framework with diverse corpora presenting a safety biasCode0
Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series ForecastingCode0
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one MapsCode0
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells—0
Real-time Drift Detection on Time-series Data—0
Causal Discovery from Conditionally Stationary Time Series—0
Time Series Analysis via Network Science: Concepts and Algorithms—0
Role of assortativity in predicting burst synchronization using echo state network—0
Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets—0
Nonparametric Tests of Conditional Independence for Time Series—0
Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem modelCode0
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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