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 2451–2475 of 6748 papers

TitleStatusHype
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity DemandCode0
Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey—0
Bayesian Online Change Point Detection for Baseline Shifts—0
An Improved Mathematical Model of Sepsis: Modeling, Bifurcation Analysis, and Optimal Control Study for Complex Nonlinear Infectious Disease System—0
Detecting CAN Masquerade Attacks with Signal Clustering Similarity—0
Unifying Epidemic Models with Mixtures—0
Applications of Signature Methods to Market Anomaly Detection—0
Churn prediction in online gambling—0
Approximate Factor Models for Functional Time SeriesCode0
Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics—0
Sales Time Series Analytics Using Deep Q-Learning—0
Second-Order Ultrasound Elastography with L1-norm Spatial Regularization—0
Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting—0
Bitcoin Price Predictive Modeling Using Expert Correction—0
Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics—0
Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies—0
A Review of Mathematical and Computational Methods in Cancer Dynamics—0
Elastic Product Quantization for Time SeriesCode0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
The Interpretability of LSTM Models for Predicting Oil Company Stocks: Impact of Correlated Features—0
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics—0
Deep Learning and Linear Programming for Automated Ensemble Forecasting and InterpretationCode0
High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series—0
Modelling matrix time series via a tensor CP-decomposition—0
Random cohort effects and age groups dependency structure for mortality modelling and forecasting: Mixed-effects time-series model approach—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