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 1601–1650 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
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
Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?—0
Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveSCode0
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
Synthetic Photovoltaic and Wind Power Forecasting Data—0
GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER—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
Seasonality, density dependence and spatial population synchrony—0
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning—0
LEAD1.0: A Large-scale Annotated Dataset for Energy Anomaly Detection in Commercial BuildingsCode1
Theory of Acceleration of Decision Making by Correlated Time Sequences—0
The amplitude modulation pattern of Gaussian noise is a fingerprint of Gaussianity—0
Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data—0
Tampered VAE for Improved Satellite Image Time Series Classification—0
Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation—0
Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States—0
Economic state classification and portfolio optimisation with application to stagflationary environments—0
A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction—0
Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full VersionCode1
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time SeriesCode0
Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan RegionCode0
A Cellular Automaton Model for the generation of Brainwaves—0
Enhancing Transformer Efficiency for Multivariate Time Series Classification—0
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional NetworksCode0
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis—0
AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning—0
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach—0
Implications of Mortality Displacement for Effect Modification and Selection Bias—0
Using Multiple Instance Learning for Explainable Solar Flare PredictionCode0
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development—0
HYDRA: Competing convolutional kernels for fast and accurate time series classificationCode1
Domino: Discovering Systematic Errors with Cross-Modal EmbeddingsCode2
Rough volatility: fact or artefact?—0
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference—0
A platform for causal knowledge representation and inference in industrial fault diagnosis based on cubic DUCG—0
Wind speed forecast using random forest learning method—0
A Deep Learning Approach to Probabilistic Forecasting of WeatherCode0
Muscle Vision: Real Time Keypoint Based Pose Classification of Physical Exercises—0
DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change SegmentationCode0
Bubble Prediction of Non-Fungible Tokens (NFTs): An Empirical InvestigationCode0
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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