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 2601–2650 of 6748 papers

TitleStatusHype
Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data—0
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks—0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Soft Sensing Transformer: Hundreds of Sensors are Worth a Single WordCode0
Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility informationCode0
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting—0
American Hate Crime Trends Prediction with Event Extraction—0
Mimic: An adaptive algorithm for multivariate time series classification—0
A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters—0
A toolkit for data-driven discovery of governing equations in high-noise regimesCode0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
Stock Portfolio Optimization Using a Deep Learning LSTM Model—0
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling—0
CoughTrigger: Earbuds IMU Based Cough Detection Activator Using An Energy-efficient Sensitivity-prioritized Time Series Classifier—0
DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting—0
Meta-Forecasting by combining Global Deep Representations with Local Adaptation—0
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time SeriesCode0
Efficacy the of Confinement Policies on the COVID-19 Spread Dynamics in the Early Period of the Pandemic—0
Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice—0
Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations—0
Predictive Auto-scaling with OpenStack MonascaCode0
Roadmap on Signal Processing for Next Generation Measurement Systems—0
Deep Learning Algorithms for Hedging with FrictionsCode0
Quality change: norm or exception? Measurement, Analysis and Detection of Quality Change in WikipediaCode0
Time Series Comparisons in Deep Space Network—0
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks—0
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
Metaphor Development in Public Discourse Using an ARIMA Time Series Analysis Approach—0
Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models—0
Nested Multiple Instance Learning with Attention MechanismsCode0
Brain dynamics via Cumulative Auto-Regressive Self-Attention—0
Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE—0
ECG synthesis with Neural ODE and GAN models—0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index—0
Robust and efficient change point detection using novel multivariate rank-energy GoF test—0
Aligned Multi-Task Gaussian Process—0
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations—0
Improved FRQI on superconducting processors and its restrictions in the NISQ era—0
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction ModelsCode0
Coresets for Time Series Clustering—0
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach—0
Multi-Task Neural ProcessesCode0
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic TomographyCode0
Warped Dynamic Linear Models for Time Series of CountsCode0
Forecasting with a Panel Tobit Model—0
GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series—0
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series 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