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 2801–2825 of 6748 papers

TitleStatusHype
A Shapelet Transform for Multivariate Time Series Classification—0
A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture—0
A cloud-IoT platform for passive radio sensing: challenges and application case studies—0
Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural Network—0
Construction of neural networks for realization of localized deep learning—0
Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model—0
A Sequential Modelling Approach for Indoor Temperature Prediction and Heating Control in Smart Buildings—0
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring—0
Construction Cost Index Forecasting: A Multi-feature Fusion Approach—0
Constructing Time Series Shape Association Measures: Minkowski Distance and Data Standardization—0
A sequential approach to calibrate ecosystem models with multiple time series data—0
Constraints on parameter choices for successful reservoir computing—0
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
A data-informed mathematical model of microglial cell dynamics during ischemic stroke in the middle cerebral artery—0
A multifactorial evaluation framework for gene regulatory network reconstruction—0
Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction—0
A Semi-Supervised Approach for Abnormal Event Prediction on Large Operational Network Time-Series Data—0
Conformal Prediction with Temporal Quantile Adjustments—0
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping—0
A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade—0
A data imputation method for multivariate time series based on generative adversarial network—0
Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reports—0
A Basic Recurrent Neural Network Model—0
A Self-Supervised Framework for Function Learning and Extrapolation—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