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 1626–1650 of 6748 papers

TitleStatusHype
Combining datasets to increase the number of samples and improve model fittingCode0
Class-Specific Explainability for Deep Time Series ClassifiersCode0
Short-term prediction of stream turbidity using surrogate data and a meta-model approach—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting—0
Self-explaining Hierarchical Model for Intraoperative Time SeriesCode0
A Clustering Algorithm for Correlation Quickest Hub Discovery Mixing Time Evolution and Random Matrix Theory—0
Multi-Task Dynamical SystemsCode0
ANFIS-based prediction of power generation for combined cycle power plant—0
An Empirical Study on How the Developers Discussed about Pandas Topics—0
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts—0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
From Rules to Regs: A Structural Topic Model of Collusion Research—0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural NetworksCode0
Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series ModelsCode0
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting—0
Biological neurons act as generalization filters in reservoir computing—0
Learning Video-independent Eye Contact Segmentation from In-the-Wild VideosCode0
Tripletformer for Probabilistic Interpolation of Irregularly sampled Time SeriesCode0
Feature Importance for Time Series Data: Improving KernelSHAP—0
The Local to Unity Dynamic Tobit Model—0
Efficient probabilistic reconciliation of forecasts for real-valued and count time series—0
Stock Volatility Prediction using Time Series and Deep Learning Approach—0
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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