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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 5901–5950 of 6748 papers

TitleStatusHype
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction—0
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction—0
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches—0
Some stylized facts of the Bitcoin market—0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1—0
Effects of pitch and timing expectancy on musical emotion—0
Hypotheses testing on infinite random graphs—0
Anomaly Detection on Graph Time Series—0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model—0
Coherent probabilistic forecasts for hierarchical time series—0
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference—0
Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates—0
Bidirectional learning for time-series models with hidden units—0
Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags—0
Tensorial Recurrent Neural Networks for Longitudinal Data Analysis—0
Spurious memory in non-equilibrium stochastic models of imitative behavior—0
Detecting and Explaining Causes From Text For a Time Series EventCode0
A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data—0
Variational online learning of neural dynamicsCode0
Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning—0
Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems—0
Copy the dynamics using a learning machine—0
Deep Forecast: Deep Learning-based Spatio-Temporal ForecastingCode0
Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder—0
Lagrange regularisation approach to compare nested data sets and determine objectively financial bubbles' inceptions—0
A signature-based machine learning model for bipolar disorder and borderline personality disorderCode0
Ultraslow diffusion in language: Dynamics of appearance of already popular adjectives on Japanese blogs—0
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network—0
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior—0
Correlations and Flow of Information between The New York Times and Stock Markets—0
Deep Learning to Attend to Risk in ICU—0
Variational approach for learning Markov processes from time series data—0
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary—0
Portfolio Risk Assessment using Copula Models—0
Distance-to-Mean Continuous Conditional Random Fields to Enhance Prediction Problem in Traffic Flow Data—0
Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution—0
Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches—0
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction—0
Initialising Kernel Adaptive Filters via Probabilistic Inference—0
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial NetworksCode0
Composition Properties of Inferential Privacy for Time-Series Data—0
Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach—0
Tailoring Artificial Neural Networks for Optimal LearningCode0
Option Pricing and Hedging for Discrete Time Autoregressive Hidden Markov ModelCode0
Causal Consistency of Structural Equation Models—0
Structured Black Box Variational Inference for Latent Time Series Models—0
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific FeaturesCode0
Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference—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