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

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Showing 3451–3500 of 6748 papers

TitleStatusHype
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds—0
Predicting Landsat Reflectance with Deep Generative FusionCode0
SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems—0
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data AnalysisCode0
Predicting the Future is like Completing a Painting!—0
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting—0
The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns—0
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems—0
Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models—0
Applying Machine Learning to Crowd-sourced Data from Earthquake DetectiveCode0
Statistical analysis of Wasserstein GANs with applications to time series forecasting—0
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition—0
Predictive Process Model Monitoring using Recurrent Neural Networks—0
Merchant Category Identification Using Credit Card Transactions—0
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
Tabular Transformers for Modeling Multivariate Time SeriesCode1
Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model—0
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetCode1
Time Series Forecasting with Stacked Long Short-Term Memory Networks—0
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic—0
Structured Self-AttentionWeights Encode Semantics in Sentiment AnalysisCode0
ORBITS: Online Recovery of Missing Blocks in Multiple Time Series StreamsCode0
Creating cloud-free satellite imagery from image time series with deep learning—0
A Lane-Changing Prediction Method Based on Temporal Convolution Network—0
Multiscale characteristics of the emerging global cryptocurrency market—0
Modern strategies for time series regression—0
Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests—0
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection—0
Augmenting transferred representations for stock classification—0
Collective Awareness for Abnormality Detection in Connected Autonomous Vehicles—0
Evaluating data augmentation for financial time series classificationCode0
Hybrid Backpropagation Parallel Reservoir Networks—0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review—0
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent KernelsCode0
Quasi-steady uptake and bacterial community assembly in a mathematical model of soil-phosphorus mobility—0
An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systems—0
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers—0
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
A Systematic Comparison of Forecasting for Gross Domestic Product in an Emergent Economy—0
Estimation of the mortality rate functions from time series field data in a stage-structured demographic model for Lobesia botrana—0
Inter-Series Attention Model for COVID-19 ForecastingCode1
Blind Deinterleaving of Signals in Time Series with Self-attention Based Soft Min-cost Flow Learning—0
Model of continuous random cascade processes in financial markets—0
Loss-analysis via Attention-scale for Physiologic Time Series—0
Towards Safe Policy Improvement for Non-Stationary MDPsCode0
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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