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 1101–1125 of 6748 papers

TitleStatusHype
Ordinal analysis of lexical patterns—0
Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization—0
Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution—0
Inter- and Intra-Series Embeddings Fusion Network for Epidemiological ForecastingCode0
We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience—0
Towards an AI-based Early Warning System for Bridge Scour—0
Predicting the Biological Classification of Cell-Cycle Regulated Genes of Saccharomyces cerevisiae using Community Detection Algorithms on Gene Co-expression Networks—0
Prediction of good reaction coordinates and future evolution of MD trajectories using Regularized Sparse Autoencoders: A novel deep learning approach—0
ArNet-ECG: Deep Learning for the Detection of Atrial Fibrillation from the Raw Electrocardiogram—0
Shapelet-Based Counterfactual Explanations for Multivariate Time SeriesCode0
Stop&Hop: Early Classification of Irregular Time SeriesCode1
AA-Forecast: Anomaly-Aware Forecast for Extreme EventsCode1
From Time Series to Networks in R with the ts2net PackageCode1
A decomposition of book structure through ousiometric fluctuations in cumulative word-time—0
Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series DataCode1
Diffusion-based Time Series Imputation and Forecasting with Structured State Space ModelsCode2
Expressing Multivariate Time Series as Graphs with Time Series Attention TransformerCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders PredictionCode0
Learning-based estimation of in-situ wind speed from underwater acoustics—0
Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutionsCode1
Network inference via process motifs for lagged correlation in linear stochastic processes—0
KDD CUP 2022 Wind Power Forecasting Team 88VIP Solution—0
Sequence Prediction Under Missing Data : An RNN Approach Without Imputation—0
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education—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