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

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Showing 2801–2850 of 6748 papers

TitleStatusHype
Fractional trends and cycles in macroeconomic time series—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Accelerating Neural ODEs Using Model Order Reduction—0
FreDo: Frequency Domain-based Long-Term Time Series Forecasting—0
Free congruence: an exploration of expanded similarity measures for time series data—0
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems—0
Frequency Domain Compact 3D Convolutional Neural Networks—0
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers—0
Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction—0
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction—0
From Generalization Analysis to Optimization Designs for State Space Models—0
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?—0
Enhance the performance of navigation: A two-stage machine learning approach—0
From learning gait signatures of many individuals to reconstructing gait dynamics of one single individual—0
Enhancement of Healthcare Data Performance Metrics using Neural Network Machine Learning Algorithms—0
From Rules to Regs: A Structural Topic Model of Collusion Research—0
From sleep medicine to medicine during sleep: A clinical perspective—0
From Static to Dynamic Node Embeddings—0
Causal Inference on Time Series using Restricted Structural Equation Models—0
From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
From time-series transcriptomics to gene regulatory networks: a review on inference methods—0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
Enhanced Cyber-Physical Security through Deep Learning Techniques—0
Conditional Generative Models for Counterfactual Explanations—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
Conditional heteroskedasticity in crypto-asset returns—0
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality—0
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network—0
Functional Bayesian Filter—0
Functional Classwise Principal Component Analysis: A Novel Classification Framework—0
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD—0
Functional Mixtures-of-Experts—0
Functional Time Series Forecasting: Functional Singular Spectrum Analysis Approaches—0
Detection and Forecasting of Extreme event in Stock Price Triggered by Fundamental, Technical, and External Factors—0
Bayesian Neural Networks for Macroeconomic Analysis—0
Fused Lasso for Feature Selection using Structural Information—0
Fusing Continuous-valued Medical Labels using a Bayesian Model—0
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning—0
Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task—0
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms—0
Fuzzy Longest Common Subsequence Matching With FCM Using R—0
FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting—0
Finite volume method network for acceleration of unsteady computational fluid dynamics: non-reacting and reacting flows—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—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