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 3226–3250 of 6748 papers

TitleStatusHype
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information—0
Reliable Fleet Analytics for Edge IoT Solutions—0
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
A Bayesian neural network predicts the dissolution of compact planetary systemsCode1
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
General Hannan and Quinn Criterion for Common Time Series—0
Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning ApproachCode0
Challenges and approaches to time-series forecasting in data center telemetry: A Survey—0
Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation—0
Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series—0
Predicting Patient Outcomes with Graph Representation LearningCode1
Data Normalization for Bilinear Structures in High-Frequency Financial Time-seriesCode1
Bootstrapping Non-Stationary Stochastic Volatility—0
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform—0
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data—0
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection—0
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI—0
Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models—0
The data synergy effects of time-series deep learning models in hydrology—0
Do We Really Need Deep Learning Models for Time Series Forecasting?Code1
A Trainable Reconciliation Method for Hierarchical Time-Series—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