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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 5101–5150 of 6748 papers

TitleStatusHype
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations—0
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals—0
Nonparametric Bayesian Sparse Graph Linear Dynamical Systems—0
Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles—0
Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clustering—0
Nonparametric Independent Component Analysis for the Sources with Mixed Spectra—0
Nonparametric risk bounds for time-series forecasting—0
Non-parametric Sparse Additive Auto-regressive Network Models—0
Nonparametric Test for Volatility in Clustered Multiple Time Series—0
Nonparametric Tests of Conditional Independence for Time Series—0
Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces—0
Non-stationary continuous dynamic Bayesian networks—0
Non-stationary dynamic Bayesian networks—0
Non-stationary Online Regression—0
NonSTOP: A NonSTationary Online Prediction Method for Time Series—0
Non-technical Loss Detection with Statistical Profile Images Based on Semi-supervised Learning—0
Non-Volatile Memory Array Based Quantization- and Noise-Resilient LSTM Neural Networks—0
Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity—0
Normalized multivariate time series causality analysis and causal graph reconstruction—0
Normalizing flows for novelty detection in industrial time series data—0
Normalizing Kalman Filters for Multivariate Time Series Analysis—0
Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models—0
Noumenal Labs White Paper: How To Build A Brain—0
Novel Fourier Quadrature Transforms and Analytic Signal Representations for Nonlinear and Non-stationary Time Series Analysis—0
Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition—0
Novel semi-metrics for multivariate change point analysis and anomaly detection—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Novel techniques for improving NNetEn entropy calculation for short and noisy time series—0
Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach—0
Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model—0
Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges—0
Nowcasting the Financial Time Series with Streaming Data Analytics under Apache Spark—0
NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading—0
Numerical approximation of hybrid Poisson-jump Ait-Sahalia-type interest rate model with delay—0
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection—0
Nyström Regularization for Time Series Forecasting—0
Objective Evaluation of Deep Visual Interpretations on Time Series Data—0
Object recognition for robotics from tactile time series data utilising different neural network architectures—0
Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks—0
Observed and estimated prevalence of Covid-19 in Italy: Is it possible to estimate the total cases from medical swabs data?—0
Observing Features of PTT Neologisms: A Corpus-driven Study with N-gram Model—0
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines—0
ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario—0
Omni-Dimensional Frequency Learner for General Time Series Analysis—0
On Adversarial Vulnerability of PHM algorithms: An Initial Study—0
On a novel training algorithm for sequence-to-sequence predictive recurrent networks—0
On Arrhythmia Detection by Deep Learning and Multidimensional Representation—0
On Attention Models for Human Activity Recognition—0
On change point detection using the fused lasso method—0
On clustering financial time series: a need for distances between dependent random variables—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