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 2876–2900 of 6748 papers

TitleStatusHype
Forecasting The JSE Top 40 Using Long Short-Term Memory Networks—0
Modeling biological networks: from single gene systems to large microbial communities—0
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement—0
SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series—0
Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model—0
Interpreting intermediate convolutional layers of generative CNNs trained on waveforms—0
Recursive input and state estimation: A general framework for learning from time series with missing data—0
Viking: Variational Bayesian Variance Tracking—0
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series DataCode1
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series ClassificationCode1
Probabilistic water demand forecasting using quantile regression algorithms—0
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants—0
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions—0
Tracking agitation in people living with dementia in a care environment—0
Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity—0
HIVE-COTE 2.0: a new meta ensemble for time series classificationCode1
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian ApproachCode0
Underwater dual-magnification imaging for automated lake plankton monitoring—0
Adversarial Sticker: A Stealthy Attack Method in the Physical WorldCode1
Process Outcome Prediction: CNN vs. LSTM (with Attention)—0
Bayesian Optimisation for a Biologically Inspired Population Neural Network—0
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulationsCode1
A Bayesian analysis of gain-loss asymmetry—0
LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer InformationCode0
A Fast Evidential Approach for Stock Forecasting—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