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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 4301–4350 of 6748 papers

TitleStatusHype
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Online Learning of the Kalman Filter with Logarithmic Regret—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
Timing Excess Returns A cross-universe approach to alpha—0
On the statistics of scaling exponents and the Multiscaling Value at Risk—0
Statistical analysis and stochastic interest rate modelling for valuing the future with implications in climate change mitigation—0
Gaussian process imputation of multiple financial series—0
Selecting time-series hyperparameters with the artificial jackknifeCode1
Exact Indexing of Time Series under Dynamic Time Warping—0
Predicting Multidimensional Data via Tensor Learning—0
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series ForecastingCode1
A review on outlier/anomaly detection in time series data—0
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems—0
Finding manoeuvre motifs in vehicle telematics—0
Time Series Alignment with Global InvariancesCode0
Autoencoder-based time series clustering with energy applications—0
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series—0
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
Improving S&P stock prediction with time series stock similarityCode1
Learning CHARME models with neural networksCode0
Online change-point detection with kernels—0
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data—0
Meta-learning framework with applications to zero-shot time-series forecastingCode1
Unsupervised non-parametric change point detection in quasi-periodic signals—0
Equivalence relations and L^p distances between time series with application to the Black Summer Australian bushfires—0
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data—0
Uncovering differential equations from data with hidden variables—0
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction—0
Sharpe Ratio Analysis in High Dimensions: Residual-Based Nodewise Regression in Factor Models—0
Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks—0
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network—0
Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks—0
Profit-oriented sales forecasting: a comparison of forecasting techniques from a business perspective—0
DYNOTEARS: Structure Learning from Time-Series DataCode1
Variable-lag Granger Causality and Transfer Entropy for Time Series AnalysisCode1
Model Extraction Attacks against Recurrent Neural Networks—0
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation—0
Two-Sample Testing for Event Impacts in Time SeriesCode0
Automated Deep Abstractions for Stochastic Chemical Reaction NetworksCode0
Semantic Discord: Finding Unusual Local Patterns for Time SeriesCode0
Towards a Kernel based Uncertainty Decomposition Framework for Data and Models—0
Signatures of brain criticality unveiled by maximum entropy analysis across cortical states—0
A Time-Series Distribution Test System Based on Real Utility Data—0
Ensemble Grammar Induction For Detecting Anomalies in Time Series—0
Bayesian Neural Architecture Search using A Training-Free Performance MetricCode1
Analysis, Online Estimation, and Validation of a Competing Virus Model—0
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information—0
Dynamic clustering of time series data—0
WISDoM: characterizing neurological timeseries with the Wishart distribution—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