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 3426–3450 of 6748 papers

TitleStatusHype
Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion—0
Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time SignalsCode0
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization—0
Deep Hedging under Rough Volatility—0
AttentionFlow: Visualising Influence in Networks of Time Series—0
Time Series Classification via Topological Data Analysis—0
Time Adaptive Gaussian Model—0
A Stochastic Time Series Model for Predicting Financial Trends using NLP—0
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder—0
Policy Analysis using Synthetic Controls in Continuous-TimeCode0
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators—0
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning—0
Stochastic Online Convex Optimization. Application to probabilistic time series forecasting—0
Impulse data models for the inverse problem of electrocardiography—0
Can Predominant Credible Information Suppress Misinformation in Crises? Empirical Studies of Tweets Related to Prevention Measures during COVID-19—0
Classification Models for Partially Ordered Sequences—0
Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting—0
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting—0
Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff Modeling based on Recurrent Neural Networks—0
Time Series (re)sampling using Generative Adversarial Networks—0
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting—0
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model—0
Reservoir Computing with Magnetic Thin Films—0
Adaptive Sequential Design for a Single Time-Series—0
Low Rank Forecasting—0
Show:102550
← PrevPage 138 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