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

Showing 59015950 of 6748 papers

TitleStatusHype
Detecting and Explaining Causes From Text For a Time Series EventCode0
Variational online learning of neural dynamicsCode0
Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning0
Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems0
Copy the dynamics using a learning machine0
Deep Forecast: Deep Learning-based Spatio-Temporal ForecastingCode0
Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder0
Lagrange regularisation approach to compare nested data sets and determine objectively financial bubbles' inceptions0
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 blogs0
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network0
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior0
Correlations and Flow of Information between The New York Times and Stock Markets0
Deep Learning to Attend to Risk in ICU0
Variational approach for learning Markov processes from time series data0
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary0
Portfolio Risk Assessment using Copula Models0
Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution0
Distance-to-Mean Continuous Conditional Random Fields to Enhance Prediction Problem in Traffic Flow Data0
Initialising Kernel Adaptive Filters via Probabilistic Inference0
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction0
Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches0
Composition Properties of Inferential Privacy for Time-Series Data0
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial NetworksCode0
Visual Analytics of Movement Pattern Based on Time-Spatial Data: A Neural Net Approach0
Tailoring Artificial Neural Networks for Optimal LearningCode0
Option Pricing and Hedging for Discrete Time Autoregressive Hidden Markov ModelCode0
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
Structured Black Box Variational Inference for Latent Time Series Models0
Causal Consistency of Structural Equation Models0
Multi-period Time Series Modeling with Sparsity via Bayesian Variational Inference0
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific FeaturesCode0
Hybrid Neural Networks for Learning the Trend in Time Series0
Probabilistic Temporal Subspace Clustering0
Modeling Sub-Event Dynamics in First-Person Action Recognition0
Learning to Generate Market Comments from Stock Prices0
Empirical analysis of daily cash flow time series and its implications for forecasting0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning0
Forecasting and Granger Modelling with Non-linear Dynamical DependenciesCode0
Introducing Data Primitives: Data Formats for the SKED Framework0
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data0
Reservoir Computing on the Hypersphere0
TimeNet: Pre-trained deep recurrent neural network for time series classificationCode0
Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO0
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach0
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings0
Infinite Mixture Model of Markov Chains0
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierCode0
Analysis of cyclical behavior in time series of stock market returns0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified