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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 1201–1250 of 6748 papers

TitleStatusHype
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images—0
Time-Varying Poisson Autoregression—0
POP: Mining POtential Performance of new fashion products via webly cross-modal query expansionCode0
Multi-temporal speckle reduction with self-supervised deep neural networks—0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
Estimating value at risk: LSTM vs. GARCH—0
Exploring Financial Networks Using Quantile Regression and Granger Causality—0
Efficiency of the Moscow Stock Exchange before 2022—0
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation—0
A Convolutional Neural Network Approach to Supernova Time-Series Classification—0
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense MechanismsCode5
Using Neural Networks by Modelling Semi-Active Shock Absorber—0
Probabilistic Reconciliation of Count Time Series—0
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers—0
Task-aware Similarity Learning for Event-triggered Time Series—0
Testing for explosive bubbles: a review—0
Transfer learning for time series classification using synthetic data generationCode1
Multiscale Causal Structure Learning—0
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting SystemCode0
Greykite: Deploying Flexible Forecasting at Scale at LinkedInCode3
Outlier detection of vital sign trajectories from COVID-19 patientsCode0
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time SeriesCode0
Rethinking Attention Mechanism in Time Series Classification—0
Spatiotemporal Propagation Learning for Network-Wide Flight Delay PredictionCode1
StockBot: Using LSTMs to Predict Stock PricesCode0
A Hybrid Approach on Conditional GAN for Portfolio Analysis—0
Synthesis of Parametric Hybrid Automata from Time SeriesCode0
Learning Deep Time-index Models for Time Series ForecastingCode2
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG—0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index—0
Dateformer: Time-modeling Transformer for Longer-term Series ForecastingCode0
IMG-NILM: A Deep learning NILM approach using energy heatmaps—0
Improved Batching Strategy For Irregular Time-Series ODE—0
Wasserstein multivariate auto-regressive models for modeling distributional time seriesCode0
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
Markovian Gaussian Process Variational Autoencoders—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
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity RecognitionCode0
Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction—0
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
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
Memory-free Online Change-point Detection: A Novel Neural Network ApproachCode1
Seasonal Encoder-Decoder Architecture for Forecasting—0
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series—0
Causal Discovery using Model Invariance through Knockoff InterventionsCode0
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in PythonCode2
Backpropagation on Dynamical Networks—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