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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 4201–4250 of 6748 papers

TitleStatusHype
R-FORCE: Robust Learning for Random Recurrent Neural NetworksCode0
Spatio-Temporal Graph Convolution for Resting-State fMRI AnalysisCode1
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction—0
Scalable Deployment of AI Time-series Models for IoT—0
TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications—0
Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework—0
Generative ODE Modeling with Known UnknownsCode1
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes—0
The Internet of Things as a Deep Neural Network—0
Deep Markov Spatio-Temporal FactorizationCode0
TSFEL: Time Series Feature Extraction LibraryCode2
One model is not enough: heterogeneity in cryptocurrencies' multifractal profiles—0
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time—0
Locating line and node disturbances in networks of diffusively coupled dynamical agents—0
Human Activity Recognition from Wearable Sensor Data Using Self-AttentionCode1
Construe: a software solution for the explanation-based interpretation of time seriesCode1
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix—0
BrazilDAM: A Benchmark dataset for Tailings Dam DetectionCode0
Market states: A new understanding—0
Time series and machine learning to forecast the water quality from satellite data—0
Drift-Adjusted And Arbitrated Ensemble Framework For Time Series Forecasting—0
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions—0
PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction—0
Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations—0
A CNN–LSTM model for gold price time-series forecasting—0
Hybrid Cryptocurrency Pump and Dump Detection—0
Deep learning-based sequential pattern mining for progressive database—0
Phase-randomised Fourier transform model for the generation of synthetic wind speeds—0
EB-dynaRE: Real-Time Adjustor for Brownian Movement with Examples of Predicting Stock Trends Based on a Novel Event-Based Supervised Learning Algorithm—0
A Time Series Approach To Player Churn and Conversion in Videogames—0
An Evaluation of Change Point Detection AlgorithmsCode1
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
Nonlinear least-squares spline fitting with variable knots—0
Dynamic Spatiotemporal Graph Neural Network with Tensor Network—0
Time Series Forecasting Using LSTM Networks: A Symbolic ApproachCode0
Linear-time inference for Gaussian Processes on one dimension—0
A tail dependence-based MST and their topological indicators in modelling systemic risk in the European insurance sector—0
Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models—0
Time series modelling to forecast the confirmed and recovered cases of COVID-19—0
FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural NetworksCode1
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure EvolutionCode1
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Adversarial Attacks on Probabilistic Autoregressive Forecasting ModelsCode1
Progressive Growing of Neural ODEs—0
Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees—0
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets—0
On the performance of deep learning models for time series classification in streamingCode1
DANTE: A framework for mining and monitoring darknet traffic—0
What went wrong and when? Instance-wise Feature Importance for Time-series Models—0
Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural NetworksCode0
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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