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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1900 of 6748 papers

TitleStatusHype
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning—0
Extending the Range of Robust PCE Inflation Measures—0
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series—0
Dynamics and triggers of misinformation on vaccines—0
Forecasting euro area inflation using a huge panel of survey expectations—0
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression ModelingCode0
An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic—0
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification—0
Time Series Prediction under Distribution Shift using Differentiable ForgettingCode0
Anomaly Detection for Fraud in Cryptocurrency Time Series—0
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images—0
Latent Space Unsupervised Semantic Segmentation—0
POP: Mining POtential Performance of new fashion products via webly cross-modal query expansionCode0
Time-Varying Poisson Autoregression—0
Multi-temporal speckle reduction with self-supervised deep neural networks—0
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation—0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
Efficiency of the Moscow Stock Exchange before 2022—0
Exploring Financial Networks Using Quantile Regression and Granger Causality—0
Estimating value at risk: LSTM vs. GARCH—0
A Convolutional Neural Network Approach to Supernova Time-Series Classification—0
Probabilistic Reconciliation of Count Time Series—0
Using Neural Networks by Modelling Semi-Active Shock Absorber—0
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers—0
Testing for explosive bubbles: a review—0
Task-aware Similarity Learning for Event-triggered Time Series—0
Multiscale Causal Structure Learning—0
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting SystemCode0
Outlier detection of vital sign trajectories from COVID-19 patientsCode0
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time SeriesCode0
StockBot: Using LSTMs to Predict Stock PricesCode0
Rethinking Attention Mechanism in Time Series Classification—0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index—0
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG—0
A Hybrid Approach on Conditional GAN for Portfolio Analysis—0
Synthesis of Parametric Hybrid Automata from Time SeriesCode0
Improved Batching Strategy For Irregular Time-Series ODE—0
Wasserstein multivariate auto-regressive models for modeling distributional time seriesCode0
Markovian Gaussian Process Variational Autoencoders—0
Dateformer: Time-modeling Transformer for Longer-term Series ForecastingCode0
IMG-NILM: A Deep learning NILM approach using energy heatmaps—0
A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture—0
Towards Neural Numeric-To-Text Generation From Temporal Personal Health DataCode0
Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction—0
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical FeaturesCode0
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical AnalysisCode0
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring ApproachCode0
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series—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