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 1601–1625 of 6748 papers

TitleStatusHype
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
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—0
Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveSCode0
Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming—0
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selection—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
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
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
LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial BuildingsCode1
Seasonality, density dependence and spatial population synchrony—0
The amplitude modulation pattern of Gaussian noise is a fingerprint of Gaussianity—0
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning—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
Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation—0
Tampered VAE for Improved Satellite Image Time Series Classification—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