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 2001–2050 of 6748 papers

TitleStatusHype
Visualizing Parliamentary Speeches as Networks: the DYLEN Tool—0
SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networks—0
Data Imputation for Multivariate Time Series Sensor Data with Large Gaps of Missing DataCode0
Sentiment Analysis of Homeric Text: The 1st Book of Iliad—0
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction—0
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series—0
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting—0
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groupsCode0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
A Review and Evaluation of Elastic Distance Functions for Time Series Clustering—0
Temporal Multiresolution Graph Neural Networks For Epidemic PredictionCode0
Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasksCode0
CHALLENGER: Training with Attribution Maps—0
Deep Generators on Commodity Markets; application to Deep Hedging—0
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units—0
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering—0
Finding Patterns in Visualized Data by Adding Redundant Visual Information—0
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
Towards Symbolic Time Series Representation Improved by Kernel Density Estimators—0
Analytics of Business Time Series Using Machine Learning and Bayesian Inference—0
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time SeriesCode0
Conformal Prediction Intervals with Temporal DependenceCode0
FreDo: Frequency Domain-based Long-Term Time Series Forecasting—0
Forecasting Multilinear Data via Transform-Based Tensor Autoregression—0
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition—0
Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning—0
Forecasting of Non-Stationary Sales Time Series Using Deep Learning—0
Signal Restoration and Channel Estimation for Channel Sounding with SDRs—0
Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies—0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection—0
Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization—0
Interpretable Feature Engineering for Time Series Predictors using Attention Networks—0
Deep Direct Discriminative Decoders for High-dimensional Time-series Data AnalysisCode0
Individual Topology Structure of Eye Movement Trajectories—0
A Novel Markov Model for Near-Term Railway Delay Prediction—0
Neural Additive Models for Nowcasting—0
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
A Subspace Method for Time Series Anomaly Detection in Cyber-Physical SystemsCode0
RiskLoc: Localization of Multi-dimensional Root Causes by Weighted Risk—0
Conformal Prediction with Temporal Quantile Adjustments—0
Persistent Homology of Coarse Grained State Space Networks—0
The Forecasting performance of the Factor model with Martingale Difference errors—0
Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder—0
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data—0
Inferring extended summary causal graphs from observational time series—0
Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow—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