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 6726–6748 of 6748 papers

TitleStatusHype
Data-driven detrending of nonstationary fractal time series with echo state networksCode0
Multi-view Integration Learning for Irregularly-sampled Clinical Time SeriesCode0
Caulking the Leakage Effect in MEEG Source Connectivity AnalysisCode0
Real-Time Anomaly Detection for Streaming AnalyticsCode0
Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable DataCode0
Multi-VQG: Generating Engaging Questions for Multiple ImagesCode0
MASA: Motif-Aware State Assignment in Noisy Time Series DataCode0
Capturing the temporal constraints of gradual patternsCode0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Sparse Algorithms for Markovian Gaussian ProcessesCode0
Time Series Prediction for Graphs in Kernel and Dissimilarity SpacesCode0
ATCN: Resource-Efficient Processing of Time Series on EdgeCode0
Efficient learning of nonlinear prediction models with time-series privileged informationCode0
Transform-Invariant Non-Parametric Clustering of Covariance Matrices and its Application to Unsupervised Joint Segmentation and Action DiscoveryCode0
Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series DataCode0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
Time Series Prediction under Distribution Shift using Differentiable ForgettingCode0
Efficient Covariance Estimation from Temporal DataCode0
Capturing Actionable Dynamics with Structured Latent Ordinary Differential EquationsCode0
Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time SeriesCode0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
N-BEATS neural network for mid-term electricity load forecastingCode0
Real-time Power System State Estimation and Forecasting via Deep Neural NetworksCode0
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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