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 2351–2400 of 6748 papers

TitleStatusHype
Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods—0
Spectral Propagation Graph Network for Few-shot Time Series Classification—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting—0
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion—0
The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting—0
Detecting Structural Breaks in Foreign Exchange Markets by using the group LASSO technique—0
Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model—0
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time seriesCode0
Machine Learning Models in Stock Market Prediction—0
Robust Anomaly Detection for Time-series Data—0
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
Functional Mixtures-of-Experts—0
Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite ImageryCode0
Evaluating conditional covariance estimates via a new targeting approach and a networks-based analysis—0
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series—0
Introducing Block-Toeplitz Covariance Matrices to Remaster Linear Discriminant Analysis for Event-related Potential Brain-computer InterfacesCode0
Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT—0
COVID-19 Hospitalizations Forecasts Using Internet Search Data—0
Review of automated time series forecasting pipelines—0
Robust Audio Anomaly Detection—0
2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log-returns: out-of-sample comparison of conditional EVT models—0
Semantic of Cloud Computing services for Time Series workflows—0
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection—0
A Machine Learning Smartphone-based Sensing for Driver Behavior Classification—0
Experimental Investigation of Variational Mode Decomposition and Deep Learning for Short-Term Multi-horizon Residential Electric Load Forecasting—0
Black-box Bayesian inference for economic agent-based models—0
Deep Learning MacroeconomicsCode0
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer—0
Comparative Study of Machine Learning Models for Stock Price Prediction—0
Imbedding Deep Neural NetworksCode0
Partial Sum Processes of Residual-Based and Wald-type Break-Point Statistics in Time Series Regression Models—0
A General Description of Growth Trends—0
Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV FeedbackCode0
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier—0
Cause-Effect Preservation and Classification using Neurochaos Learning—0
Dynamic Temporal Reconciliation by Reinforcement learning—0
Robust Augmentation for Multivariate Time Series Classification—0
Unsupervised Change Detection using DRE-CUSUM—0
Online Change Point Detection for Weighted and Directed Random Dot Product GraphsCode0
Learning Mixtures of Linear Dynamical Systems—0
S^3NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks—0
Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models—0
Differentially-Private Heat and Electricity Markets Coordination—0
Multiscaling and rough volatility: an empirical investigation—0
Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection—0
Estimating and backtesting risk under heavy tails—0
Regime recovery using implied volatility in Markov modulated market modelCode0
Neural Architecture Searching for Facial Attributes-based Depression Recognition—0
COVID-19 forecasting using new viral variants and vaccination effectiveness models—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