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 976–1000 of 6748 papers

TitleStatusHype
Text Classification: Neural Networks VS Machine Learning Models VS Pre-trained Models—0
A Survey on Time-Series Distance Measures—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Hidformer: Transformer-Style Neural Network in Stock Price Forecasting—0
Adopting Trustworthy AI for Sleep Disorder Prediction: Deep Time Series Analysis with Temporal Attention Mechanism and Counterfactual Explanations—0
CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation—0
GG-SSMs: Graph-Generating State Space Models—0
Are Large Language Models Useful for Time Series Data Analysis?—0
GAF-FusionNet: Multimodal ECG Analysis via Gramian Angular Fields and Split AttentionCode0
UTSD: Unified Time Series Diffusion Model—0
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis—0
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series—0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant informationCode0
Multiscale Dubuc: A New Similarity Measure for Time SeriesCode0
Machine Learning Algorithms to Assess Site Closure Time Frames for Soil and Groundwater ContaminationCode0
Sparse Interval-valued Time Series Modeling with Machine Learning—0
How to quantify interaction strengths? A critical rethinking of the interaction Jacobian and evaluation methods for non-parametric inference in time series analysis—0
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
bursty_dynamics: A Python Package for Exploring the Temporal Properties of Longitudinal Data—0
Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis—0
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-SeriesCode0
Higher-order Cross-structural Embedding Model for Time Series Analysis—0
Gradient-free training of recurrent neural networksCode0
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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