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 4801–4850 of 6748 papers

TitleStatusHype
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer—0
Metric Learning for Temporal Sequence Alignment—0
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series—0
MIDAS: Deep learning human action intention prediction from natural eye movement patterns—0
Mimic: An adaptive algorithm for multivariate time series classification—0
Min(d)ing the President: A text analytic approach to measuring tax news—0
Mind Your POV: Convergence of Articles and Editors Towards Wikipedia's Neutrality Norm—0
Minimax Concave Penalty Regularized Adaptive System Identification—0
Minimax Time Series Prediction—0
Mining and modeling complex leadership-followership dynamics of movement data—0
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting—0
Mining Illegal Insider Trading of Stocks: A Proactive Approach—0
Mining linguistic tone patterns with symbolic representation—0
Social Media Information Sharing for Natural Disaster Response—0
Mining Sub-Interval Relationships In Time Series Data—0
Mining the Mind: Linear Discriminant Analysis of MEG source reconstruction time series supports dynamic changes in deep brain regions during meditation sessions—0
MissFormer: (In-)attention-based handling of missing observations for trajectory filtering and prediction—0
Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes—0
Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Space—0
Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation—0
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data—0
Mixed Membership Models for Time Series—0
Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series—0
Mixed pooling of seasonality for time series forecasting: An application to pallet transport data—0
Mixing Times and Structural Inference for Bernoulli Autoregressive Processes—0
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data—0
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)—0
Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series—0
MLAT: Metric Learning for kNN in Streaming Time Series—0
MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data—0
Mobile Mapping Mesh Change Detection and Update—0
Mod-DeepESN: Modular Deep Echo State Network—0
On Robustness of Principal Component Regression—0
Model-Attentive Ensemble Learning for Sequence Modeling—0
Model-Based Clustering and Classification of Functional Data—0
Model-based clustering and segmentation of time series with changes in regime—0
Model-based clustering with Hidden Markov Model regression for time series with regime changes—0
Model-Based Reinforcement Learning via Stochastic Hybrid Models—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Model-Coupled Autoencoder for Time Series Visualisation—0
Model Extraction Attacks against Recurrent Neural Networks—0
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing—0
Model-free prediction of emergence of extreme events in a parametrically driven nonlinear dynamical system by Deep Learning—0
Model-free prediction of noisy chaotic time series by deep learning—0
Model identification for ARMA time series through convolutional neural networks—0
Model inference for Ordinary Differential Equations by parametric polynomial kernel regression—0
Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning—0
Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN—0
Modeling Atmospheric Data and Identifying Dynamics: Temporal Data-Driven Modeling of Air Pollutants—0
Modeling biological networks: from single gene systems to large microbial communities—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