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 2176–2200 of 6748 papers

TitleStatusHype
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS—0
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time SeriesCode0
Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning—0
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding—0
Bridging the Gap: Decoding the Intrinsic Nature of Time in Market Data—0
Data-driven Influence Based Clustering of Dynamical Systems—0
Asymptotic Theory for Unit Root Moderate Deviations in Quantile Autoregressions and Predictive Regressions—0
Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming—0
Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection—0
Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveSCode0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting—0
Forestry digital twin with machine learning in Landsat 7 data—0
Calibration window selection based on change-point detection for forecasting electricity prices—0
Probabilistic AutoRegressive Neural Networks for Accurate Long-range ForecastingCode0
GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER—0
Synthetic Photovoltaic and Wind Power Forecasting Data—0
When to Classify Events in Open Times Series?—0
Quantum open system identification via global optimization: Optimally accurate Markovian models of open systems from time-series dataCode0
Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation—0
The amplitude modulation pattern of Gaussian noise is a fingerprint of Gaussianity—0
Tampered VAE for Improved Satellite Image Time Series Classification—0
Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data—0
Theory of Acceleration of Decision Making by Correlated Time Sequences—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