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 1376–1400 of 6748 papers

TitleStatusHype
A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM—0
A Spectral Enabled GAN for Time Series Data Generation—0
A Spectral Algorithm for Learning Hidden Markov Models—0
A Multi-View Framework for BGP Anomaly Detection via Graph Attention Network—0
COVID-19: Tail Risk and Predictive Regressions—0
COVID-19: The extraction of the effective reproduction number from the time series of new cases—0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
A Multi-Variate Triple-Regression Forecasting Algorithm for Long-Term Customized Allergy Season Prediction—0
A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19—0
A Multi-Scale Tensor Network Architecture for Classification and Regression—0
A Deep Learning Approach for COVID-19 Trend Prediction—0
A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting—0
A Soft Computing Approach for Selecting and Combining Spectral Bands—0
A multi-scale symmetry analysis of uninterrupted trends returns of daily financial indices—0
A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads—0
A Clustering Algorithm for Correlation Quickest Hub Discovery Mixing Time Evolution and Random Matrix Theory—0
Differential Recurrent Neural Network and its Application for Human Activity Recognition—0
COVID-19 forecasting using new viral variants and vaccination effectiveness models—0
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution—0
A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting—0
A simulation of the insurance industry: The problem of risk model homogeneity—0
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter—0
A Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc—0
A similarity measurement for time series and its application to the stock market—0
A Multi-modal Deep Learning Model for Video Thumbnail Selection—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