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 2626–2650 of 6748 papers

TitleStatusHype
Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning—0
Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery—0
A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas—0
A Survey on Time-Series Distance Measures—0
Analysis of Brain States from Multi-Region LFP Time-Series—0
A Deep Learning Forecaster with Exogenous Variables for Day-Ahead Locational Marginal Price—0
Critical Transitions in Intensive Care Units: A Sepsis Case Study—0
A Comparative Analysis of Machine Learning and Grey Models—0
A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting—0
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series—0
Analysis of Blink Rate Variability during reading and memory testing—0
Creating Disasters: Recession Forecasting with GAN-Generated Synthetic Time Series Data—0
Creating cloud-free satellite imagery from image time series with deep learning—0
A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models—0
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution—0
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide—0
Analysis of bio-electro-chemical signals from passive sweat-based wearable electro-impedance spectroscopy (EIS) towards assessing blood glucose modulations—0
A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data—0
A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM—0
A Bayesian Approach to Sparse plus Low rank Network Identification—0
COVID-19: The extraction of the effective reproduction number from the time series of new cases—0
COVID-19: Tail Risk and Predictive Regressions—0
COVID-19 forecasting using new viral variants and vaccination effectiveness models—0
A Survey on State-of-the-art Deep Learning Applications and Challenges—0
Analysis of bank leverage via dynamical systems and deep neural networks—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