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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 5401–5425 of 6748 papers

TitleStatusHype
MASA: Motif-Aware State Assignment in Noisy Time Series DataCode0
Deep Recurrent Electricity Theft Detection in AMI Networks with Random Tuning of Hyper-parameters—0
From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning—0
Bayesian Nonparametric Spectral EstimationCode0
A Memory-Network Based Solution for Multivariate Time-Series ForecastingCode0
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing—0
Scalable Learning in Reproducing Kernel Krein Spaces—0
VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading—0
A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services—0
Proximity Forest: An effective and scalable distance-based classifier for time seriesCode1
Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis—0
Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results—0
Elastic bands across the path: A new framework and methods to lower bound DTWCode0
Exponential inequalities for nonstationary Markov Chains—0
Testing of Binary Regime Switching Models using Squeeze Duration Analysis—0
Predicting Extubation Readiness in Extreme Preterm Infants based on Patterns of Breathing—0
SeriesNet:A Generative Time Series Forecasting Model—0
Optimizing the Union of Intersections LASSO (UoI_LASSO) and Vector Autoregressive (UoI_VAR) Algorithms for Improved Statistical Estimation at Scale—0
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks—0
Data Consistency Approach to Model ValidationCode0
Short-term load forecasting using optimized LSTM networks based on EMD—0
LARNN: Linear Attention Recurrent Neural NetworkCode0
Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach—0
Deep Learning for Energy Markets—0
Development and Evaluation of Recurrent Neural Network based Models for Hourly Traffic Volume and AADT Prediction—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