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 2401–2425 of 6748 papers

TitleStatusHype
Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks—0
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series—0
Augmenting transferred representations for stock classification—0
Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders—0
A comparison among some Hurst exponent approaches to predict nascent bubbles in 500 company stocks—0
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Deep COVID-19 Forecasting for Multiple States with Data Augmentation—0
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection—0
Deep Convolutional Neural Network for Non-rigid Image Registration—0
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—0
Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems—0
A Two-Way Transformed Factor Model for Matrix-Variate Time Series—0
Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network—0
Deep Classification of Epileptic Signals—0
Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis—0
A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface—0
An Efficient Federated Distillation Learning System for Multi-task Time Series Classification—0
Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning—0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information—0
A Two-Stage Coordinative Zonal Volt/VAR Control Scheme for Distribution Systems with High Inverter-based Resources—0
Deep Canonical Time Warping—0
Deep Canonically Correlated LSTMs—0
A Two-layer Approach for Estimating Behind-the-Meter PV Generation Using Smart Meter Data—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