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 3051–3100 of 6748 papers

TitleStatusHype
Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio—0
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data—0
Hierarchically Regularized Deep Forecasting—0
Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification—0
Hierarchical Multi-resolution Mesh Networks for Brain Decoding—0
A statistical test of market efficiency based on information theory—0
Hierarchical Quickest Change Detection via Surrogates—0
Hierarchical regularization networks for sparsification based learning on noisy datasets—0
Causal Hidden Markov Model for Time Series Disease Forecasting—0
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data—0
Hierarchical Time Series Forecasting with Bayesian Modeling—0
High Dimensional Forecasting via Interpretable Vector Autoregression—0
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting—0
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions—0
Correlations and Flow of Information between The New York Times and Stock Markets—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series—0
High-Dimensional Granger Causality for Climatic Attribution—0
Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis—0
End-to-end NILM System Using High Frequency Data and Neural Networks—0
Cost-Effective Bad Synchrophasor Data Detection Based on Unsupervised Time Series Data Analytics—0
High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves—0
High Dimensional Time Series Generators—0
High-dimensional Time Series Prediction with Missing Values—0
High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods—0
Higher-order Cross-structural Embedding Model for Time Series Analysis—0
HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs—0
High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach—0
A Novel Framework for Handling Sparse Data in Traffic Forecast—0
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model—0
Improved Predictive Deep Temporal Neural Networks with Trend Filtering—0
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding—0
A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming—0
Automatic Model Building in GEFCom 2017 Qualifying Match—0
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator—0
High-recall causal discovery for autocorrelated time series with latent confounders—0
Emulating dynamic non-linear simulators using Gaussian processes—0
High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production—0
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News—0
HiPPO-KAN: Efficient KAN Model for Time Series Analysis—0
Improved PAC-Bayesian Bounds for Linear Regression—0
Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics—0
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting—0
Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecasting—0
Empowering Time Series Analysis with Large Language Models: A Survey—0
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation 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