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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 5651–5700 of 6748 papers

TitleStatusHype
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects—0
CDPS: Constrained DTW-Preserving Shapelets—0
CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs—0
Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems—0
Cell Line Classification Using Electric Cell-substrate Impedance Sensing (ECIS)—0
Cellular reprogramming dynamics follow a simple one-dimensional reaction coordinate—0
Central and Non-central Limit Theorems arising from the Scattering Transform and its Neural Activation Generalization—0
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction—0
CHALLENGER: Training with Attribution Maps—0
Challenges and approaches to time-series forecasting in data center telemetry: A Survey—0
Challenges in Forecasting Malicious Events from Incomplete Data—0
Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data—0
Change of persistence in European electricity spot prices—0
Changepoint Analysis of Topic Proportions in Temporal Text Data—0
Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees—0
Change Point Detection via Multivariate Singular Spectrum Analysis—0
Channel-Based Attention for LCC Using Sentinel-2 Time Series—0
Channel masking for multivariate time series shapelets—0
"Chaos" in energy and commodity markets: a controversial matter—0
Chaos in Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes—0
Chaos may enhance expressivity in cerebellar granular layer—0
Chaotic Neuronal Oscillations in Spontaneous Cortical-Subcortical Networks—0
Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks—0
Chaotic Variational Auto encoder-based Adversarial Machine Learning—0
Characterization of catastrophic instabilities: Market crashes as paradigm—0
Characterization of causal ancestral graphs for time series with latent confounders—0
Characterization of electric consumers through an automated clustering pipeline—0
A decomposition of book structure through ousiometric fluctuations in cumulative word-time—0
Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States—0
Characters as Graphs: Recognizing Online Handwritten Chinese Characters via Spatial Graph Convolutional Network—0
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings—0
Chatter Classification in Turning Using Machine Learning and Topological Data Analysis—0
Chatter Detection in Turning Using Machine Learning and Similarity Measures of Time Series via Dynamic Time Warping—0
Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data—0
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes—0
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction—0
ChunkFormer: Learning Long Time Series with Multi-stage Chunked Transformer—0
Churn prediction in online gambling—0
Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning—0
Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction—0
CLARE-GAN: GENERATION OF CLASS-SPECIFIC TIME SERIES—0
Classification des Séries Temporelles Incertaines par Transformation Shapelet—0
Classification Models for Partially Ordered Sequences—0
Classification of chaotic time series with deep learning—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
A new hazard event classification model via deep learning and multifractal—0
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks—0
Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Classification of Stochastic Processes with Topological Data Analysis—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