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 1501–1525 of 6748 papers

TitleStatusHype
Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods—0
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers—0
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data—0
Autoregressive GNN-ODE GRU Model for Network Dynamics—0
Class-Specific Attention (CSA) for Time-Series Classification—0
Step Counting with Attention-based LSTMCode0
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart CitiesCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
Fractional integration and cointegration—0
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift—0
Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography—0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
Cointegration with Occasionally Binding Constraints—0
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic—0
Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM—0
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network—0
Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP)—0
Graph Filters for Signal Processing and Machine Learning on Graphs—0
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk PredictionCode0
Parameter-Covariance Maximum Likelihood Estimation—0
Motor imagery classification using EEG spectrograms—0
Multi-VQG: Generating Engaging Questions for Multiple ImagesCode0
Temporal patterns in insulin needs for Type 1 diabetesCode0
Similarity-based Feature Extraction for Large-scale Sparse Traffic ForecastingCode0
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOpsCode0
Show:102550
← PrevPage 61 of 270Next →
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