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 651–700 of 6748 papers

TitleStatusHype
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition—0
Security and Interpretability in Automotive Systems—0
Machine Learning with Probabilistic Law Discovery: A Concise Introduction—0
Few-shot human motion prediction for heterogeneous sensorsCode0
A Query-Response Causal Analysis of Reaction Events in Biochemical Reaction Networks—0
Temporal Disaggregation of the Cumulative Grass Growth—0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction—0
A Pattern Discovery Approach to Multivariate Time Series Forecasting—0
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
Dynamic Sparse Network for Time Series Classification: Learning What to "see''Code1
Page time and the order parameter for a consciousness state—0
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load ForecastingCode0
Reservoir Computing Using Complex Systems—0
Twitter's Agenda-Setting Role: A Study of Twitter Strategy for Political Diversion—0
Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models—0
An ensemble neural network approach to forecast Dengue outbreak based on climatic conditionCode0
Convolution-enhanced Evolving Attention NetworksCode1
Integrating Multimodal Data for Joint Generative Modeling of Complex DynamicsCode1
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries ForecastingCode1
Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model—0
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
Multi-Level Association Rule Mining for Wireless Network Time Series Data—0
Adaptive Multi-Agent Continuous Learning SystemCode0
On LASSO for High Dimensional Predictive Regression—0
Design-time Fashion Popularity Forecasting in VR Environments—0
Temporal Weights—0
Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series—0
Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
On Mini-Batch Training with Varying Length Time SeriesCode0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Nonparametric Independent Component Analysis for the Sources with Mixed Spectra—0
Land use/land cover dynamics on vulnerable regions in Uruguay approached by a method combining Maximum Entropy and Population Dynamics—0
Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management—0
Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories—0
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal ModelingCode1
Multi-Dimensional Self Attention based Approach for Remaining Useful Life EstimationCode1
Neural Continuous-Time Markov Models—0
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period—0
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data: A Koopman Operator Approach—0
Towards Better Long-range Time Series Forecasting using Generative Forecasting—0
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series—0
Unsupervised Flood Detection on SAR Time Series—0
Bi-LSTM Price Prediction based on Attention Mechanism—0
Time series numerical association rule mining variants in smart agriculture—0
Learning State Transition Rules from Hidden Layers of Restricted Boltzmann Machines—0
Sequential Predictive Conformal Inference for Time SeriesCode1
Phase2vec: Dynamical systems embedding with a physics-informed convolutional networkCode1
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time SeriesCode0
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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