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 1451–1475 of 6748 papers

TitleStatusHype
Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model—0
Construction of neural networks for realization of localized deep learning—0
Data Augmentation for Multivariate Time Series Classification: An Experimental Study—0
Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural Network—0
Data Curves Clustering Using Common Patterns Detection—0
Contemporary machine learning: a guide for practitioners in the physical sciences—0
Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community—0
Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context—0
Context-Aware Ensemble Learning for Time Series—0
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions—0
A Signal Detection Scheme Based on Deep Learning in OFDM Systems—0
Context Dependent Semantic Parsing over Temporally Structured Data—0
Context-invariant, multi-variate time series representations—0
A Multi-modal Deep Learning Model for Video Thumbnail Selection—0
A similarity measurement for time series and its application to the stock market—0
A cloud-IoT platform for passive radio sensing: challenges and application case studies—0
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter—0
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions—0
Continual Learning Using Bayesian Neural Networks—0
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE—0
A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Show:102550
← PrevPage 59 of 270Next →
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