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 4076–4100 of 6748 papers

TitleStatusHype
Comparing Lexical Usage in Political Discourse across Diachronic Corpora—0
Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining—0
Supervised Feature Subset Selection and Feature Ranking for Multivariate Time Series without Feature Extraction—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
Attentive Weakly Supervised land cover mapping for object-based satellite image time series data with spatial interpretation—0
Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time SeriesCode0
End-to-end NILM System Using High Frequency Data and Neural Networks—0
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems—0
Multi-Decoder RNN Autoencoder Based on Variational Bayes Method—0
Mapping Coupled Time-series Onto Complex Network—0
SARS-COV-2 Pandemic: Understanding the Impact of Lockdown in the Most Affected States of India—0
Effects of weather and policy intervention on COVID-19 infection in Ghana—0
Classifying Image Sequences of Astronomical Transients with Deep Neural Networks—0
Time Series Forecasting With Deep Learning: A Survey—0
Application of Deep Interpolation Network for Clustering of Physiologic Time Series—0
Adaptive model selection in photonic reservoir computing by reinforcement learning—0
Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks—0
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection—0
Forecasting in Non-stationary Environments with Fuzzy Time SeriesCode1
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
Uncovering the Dynamics of Correlation Structures Relative to the Collective Market Motion—0
A dynamic conditional approach to portfolio weights forecasting—0
Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media—0
The new face of multifractality: Multi-branchedness and the phase transitions in time series of mean inter-event times—0
Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand—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