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 1401–1450 of 6748 papers

TitleStatusHype
Deep Generators on Commodity Markets; application to Deep Hedging—0
Finding Patterns in Visualized Data by Adding Redundant Visual Information—0
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering—0
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)Code1
Topological Hidden Markov Models—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes—0
Sparse Graph Learning from Spatiotemporal Time SeriesCode1
Are Transformers Effective for Time Series Forecasting?Code4
Learning the spatio-temporal relationship between wind and significant wave height using deep learningCode1
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse ObservationsCode1
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators—0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
Conformal Prediction Intervals with Temporal DependenceCode0
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network GenerationCode1
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time SeriesCode0
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning—0
FreDo: Frequency Domain-based Long-Term Time Series Forecasting—0
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
Forecasting Multilinear Data via Transform-Based Tensor Autoregression—0
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition—0
Interpretable Feature Engineering for Time Series Predictors using Attention Networks—0
Signal Restoration and Channel Estimation for Channel Sounding with SDRs—0
Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization—0
Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies—0
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Forecasting of Non-Stationary Sales Time Series Using Deep Learning—0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection—0
Time-series Transformer Generative Adversarial NetworksCode3
Deep Direct Discriminative Decoders for High-dimensional Time-series Data AnalysisCode0
A Novel Markov Model for Near-Term Railway Delay Prediction—0
Individual Topology Structure of Eye Movement Trajectories—0
Persistent Homology of Coarse Grained State Space Networks—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
The Forecasting performance of the Factor model with Martingale Difference errors—0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
Self-Supervised Time Series Representation Learning via Cross Reconstruction TransformerCode1
Conformal Prediction with Temporal Quantile Adjustments—0
RiskLoc: Localization of Multi-dimensional Root Causes by Weighted Risk—0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
Neural Additive Models for Nowcasting—0
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
Time Series Anomaly Detection via Reinforcement Learning-Based Model SelectionCode1
Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow—0
Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data—0
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Inferring extended summary causal graphs from observational time series—0
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems—0
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics—0
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints—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