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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 3251–3300 of 6748 papers

TitleStatusHype
Data-Driven Copy-Paste Imputation for Energy Time SeriesCode0
Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data—0
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks—0
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph—0
Parkinson's Disease Diagnosis Using Deep Learning—0
Silicon Photonic Microring Based Chip-Scale Accelerator for Delayed Feedback Reservoir Computing—0
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network—0
Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19—0
Leveraging latent persistency in United States patent and trademark applications to gain insight into the evolution of an innovation-driven economy—0
Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural NetworksCode1
Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion—0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
The 2020 Global Stock Market Crash: Endogenous or Exogenous?—0
Detecting residues of cosmic events using residual neural network—0
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data—0
Recurrent Neural Network Architecture based on Dynamic Systems Theory for Data Driven Modelling of Complex Physical Systems—0
"Hey, that's not an ODE'": Faster ODE Adjoints with 12 Lines of Code—0
MODALS: Modality-agnostic Automated Data Augmentation in the Latent SpaceCode1
Graph Edit NetworksCode0
GenAD: General Representations of Multivariate Time Series for Anomaly Detection—0
CLARE-GAN: GENERATION OF CLASS-SPECIFIC TIME SERIES—0
Anomaly detection and regime searching in fitness-tracker data—0
Latent Convergent Cross Mapping—0
A Multi-Modal and Multitask Benchmark in the Clinical Domain—0
Generative Time-series Modeling with Fourier Flows—0
Time Series Counterfactual Inference with Hidden Confounders—0
Symmetry-Augmented Representation for Time Series—0
Latent Space Semi-Supervised Time Series Data Clustering—0
Jumpy Recurrent Neural Networks—0
Anomaly detection in dynamical systems from measured time series—0
An Euler-based GAN for time series—0
Sparsifying Networks via Subdifferential Inclusion—0
Transitional Dynamics of the Saving Rate and Economic Growth—0
How to Identify Investor's types in real financial markets by means of agent based simulation—0
Indirect Measurement of Hepatic Drug Clearance by Fitting Dynamical Models—0
A Multi-modal Deep Learning Model for Video Thumbnail Selection—0
Algorithms for Learning Graphs in Financial MarketsCode0
Ensembles of Localised Models for Time Series ForecastingCode1
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset—0
Time-Transformed Test for the Explosive Bubbles under Non-stationary Volatility—0
Scalable and Hybrid Ensemble-Based Causality Discovery—0
Memory-Gated Recurrent NetworksCode0
Global Models for Time Series Forecasting: A Simulation StudyCode0
General Domain Adaptation Through Proportional Progressive Pseudo LabelingCode0
Machine Learning Advances for Time Series Forecasting—0
Towards Automated Satellite Conjunction Management with Bayesian Deep LearningCode1
Causal Inference from Slowly Varying Nonstationary Processes—0
Time Series Domain Adaptation via Sparse Associative Structure Alignment—0
Generating Long-term Continuous Multi-type Generation Profiles—0
Method for estimating hidden structures determined by unidentifiable state-space models and time-series data based on the Groebner basis—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