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 4726–4750 of 6748 papers

TitleStatusHype
Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles—0
Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands—0
Machine-learning inference of fluid variables from data using reservoir computing—0
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers—0
Machine learning methods for modelling and analysis of time series signals in geoinformatics—0
Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks—0
Machine learning methods for multimedia information retrieval—0
Machine Learning Models in Stock Market Prediction—0
Machine learning models show similar performance to Renewables.ninja for generation of long-term wind power time series even without location information—0
Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing—0
Machine Learning Prediction of Time-Varying Rayleigh Channels—0
Machine learning structure preserving brackets for forecasting irreversible processes—0
Machine Learning with Probabilistic Law Discovery: A Concise Introduction—0
Machine olfaction using time scattering of sensor multiresolution graphs—0
Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian VAR approach—0
Macroeconomic forecasting with LSTM and mixed frequency time series data—0
Macro-Economic Time Series Modeling and Interaction Networks—0
Magnetic Field Sensing for Pedestrian and Robot Indoor Positioning—0
Making Good on LSTMs' Unfulfilled Promise—0
Making the Dynamic Time Warping Distance Warping-Invariant—0
MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs—0
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS—0
Mapping Coupled Time-series Onto Complex Network—0
Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution—0
Marginalised Spectral Mixture Kernels with Nested Sampling—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