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 4351–4400 of 6748 papers

TitleStatusHype
Bayesian nonparametric shared multi-sequence time series segmentation—0
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methodsCode0
Multi-label Prediction in Time Series Data using Deep Neural Networks—0
Machine Learning for a Music Glove Instrument—0
Semi-metric portfolio optimization: a new algorithm reducing simultaneous asset shocks—0
Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture modelsCode0
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence CaseCode1
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data—0
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting—0
Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings—0
A Multi-Scale Tensor Network Architecture for Classification and Regression—0
Causality based Feature Fusion for Brain Neuro-Developmental Analysis—0
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems—0
Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification—0
A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour—0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network—0
Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio EstimationCode1
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization—0
Human-like Time Series Summaries via Trend Utility Estimation—0
Stream-Flow Forecasting of Small Rivers Based on LSTM—0
Inferring Individual Level Causal Models from Graph-based Relational Time Series—0
Mind the gap: an experimental evaluation of imputation of missing values techniques in time seriesCode1
For2For: Learning to forecast from forecasts—0
IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach—0
Relational State-Space Model for Stochastic Multi-Object Systems—0
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learningCode0
Projection assisted Dynamic Mode Decomposition of large scale data—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—0
Temporally Folded Convolutional Neural Networks for Sequence Forecasting—0
Forecasting NIFTY 50 benchmark Index using Seasonal ARIMA time series models—0
Supervised Hyperalignment for multi-subject fMRI data alignment—0
Explainable Deep Convolutional Candlestick LearnerCode1
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
Discovering Nonlinear Relations with Minimum Predictive Information RegularizationCode1
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering—0
Prediction of MRI Hardware Failures based on Image Features using Time Series Classification—0
Data Curves Clustering Using Common Patterns Detection—0
Temporal Tensor Transformation Network for Multivariate Time Series Prediction—0
Root Cause Detection Among Anomalous Time Series Using Temporal State AlignmentCode1
Biologically-Motivated Deep Learning Method using Hierarchical Competitive LearningCode0
Source Model Selection for Deep Learning in the Time Series DomainCode1
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability—0
A Deep Structural Model for Analyzing Correlated Multivariate Time Series—0
Learnable Group Transform For Time-SeriesCode0
Cost-effective Interactive Attention Learning with Neural Attention ProcessCode0
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations—0
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-timeCode0
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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