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

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Showing 651–675 of 6748 papers

TitleStatusHype
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition—0
Security and Interpretability in Automotive Systems—0
Few-shot human motion prediction for heterogeneous sensorsCode0
Machine Learning with Probabilistic Law Discovery: A Concise Introduction—0
A Query-Response Causal Analysis of Reaction Events in Biochemical Reaction Networks—0
Temporal Disaggregation of the Cumulative Grass Growth—0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction—0
A Pattern Discovery Approach to Multivariate Time Series Forecasting—0
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
Dynamic Sparse Network for Time Series Classification: Learning What to "see''Code1
Page time and the order parameter for a consciousness state—0
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load ForecastingCode0
Reservoir Computing Using Complex Systems—0
Twitter's Agenda-Setting Role: A Study of Twitter Strategy for Political Diversion—0
Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models—0
An ensemble neural network approach to forecast Dengue outbreak based on climatic conditionCode0
Convolution-enhanced Evolving Attention NetworksCode1
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries ForecastingCode1
Multi-Level Association Rule Mining for Wireless Network Time Series Data—0
Integrating Multimodal Data for Joint Generative Modeling of Complex DynamicsCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model—0
Adaptive Multi-Agent Continuous Learning SystemCode0
Design-time Fashion Popularity Forecasting in VR Environments—0
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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