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 5876–5900 of 6748 papers

TitleStatusHype
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning—0
Motif-based Rule Discovery for Predicting Real-valued Time Series—0
On Early-stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners—0
Discovering Potential Correlations via Hypercontractivity—0
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application—0
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications—0
Support Spinor Machine—0
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting—0
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?—0
RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process—0
Tensor Representation in High-Frequency Financial Data for Price Change Prediction—0
Towards social pattern characterization in egocentric photo-streams—0
Boosting the kernelized shapelets: Theory and algorithms for local features—0
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks—0
Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision—0
Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media—0
Classifying Frames at the Sentence Level in News Articles—0
A State-Space Approach to Dynamic Nonnegative Matrix Factorization—0
Can Agent-Based Models Probe Market Microstructure?—0
Interpretable Categorization of Heterogeneous Time Series Data—0
On the Reconstruction Risk of Convolutional Sparse Dictionary LearningCode0
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series—0
Explainable Recommendation: Theory and Applications—0
Boltzmann machines for time-series—0
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection—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