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 4351–4400 of 6748 papers

TitleStatusHype
Influence of Mobility Restrictions on Transmission of COVID-19 in the state of Maryland -- the USA—0
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models—0
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers—0
InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers—0
Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem—0
Information-Aware Time Series Meta-Contrastive Learning—0
Information flow networks of Chinese stock market sectors—0
Information theoretical study of cross-talk mediated signal transduction in MAPK pathways—0
Information Theoretic Measures of Causal Influences during Transient Neural Events—0
Information Theory Inspired Pattern Analysis for Time-series Data—0
Initial conditions in the neural field model—0
Initialising Kernel Adaptive Filters via Probabilistic Inference—0
Initialization matters: Orthogonal Predictive State Recurrent Neural Networks—0
Initialization of multilayer forecasting artifical neural networks—0
Initializing LSTM internal states via manifold learning—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting—0
Input Sequence and Parameter Estimation in Impulsive Biomedical Models—0
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance—0
In Situ 3D Spatiotemporal Measurement of Soluble Biomarkers in Organoid Culture—0
In-situ animal behavior classification using knowledge distillation and fixed-point quantization—0
In situ process quality monitoring and defect detection for direct metal laser melting—0
Inspection of methods of empirical mode decomposition—0
Instance Explainable Temporal Network For Multivariate Timeseries—0
Instantaneous Modelling and Reverse Engineering of DataConsistent Prime Models in Seconds!—0
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education—0
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware—0
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods—0
Integrated information and dimensionality in continuous attractor dynamics—0
Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining—0
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions—0
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction—0
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis—0
Interdependency between the Stock Market and Financial News—0
Interpolation and Gap Filling of Landsat Reflectance Time Series—0
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series—0
Interpretable Categorization of Heterogeneous Time Series Data—0
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques—0
Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
Interpretable Feature Construction for Time Series Extrinsic Regression—0
Interpretable Feature Engineering for Time Series Predictors using Attention Networks—0
Interpretable Latent Variables in Deep State Space Models—0
Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications—0
Interpretable Models for Understanding Immersive Simulations—0
Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks—0
Interpretable Neural Networks for Panel Data Analysis in Economics—0
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories—0
Interpretable Super-Resolution via a Learned Time-Series Representation—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