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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 1401–1450 of 6748 papers

TitleStatusHype
The Functional Wiener Filter—0
Behave-XAI: Deep Explainable Learning of Behavioral Representational Data—0
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters—0
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time SeriesCode0
Label-Efficient Interactive Time-Series Anomaly Detection—0
Deep Temporal Contrastive Clustering—0
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals—0
Robustifying Markowitz—0
Semi-supervised multiscale dual-encoding method for faulty traffic data detection—0
Anomaly detection in laser-guided vehicles' batteries: a case study—0
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies—0
Modeling Time-Series and Spatial Data for Recommendations and Other Applications—0
Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning—0
Security and Interpretability in Automotive Systems—0
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition—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
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriesCode0
Temporal Disaggregation of the Cumulative Grass Growth—0
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction—0
A Pattern Discovery Approach to Multivariate Time Series Forecasting—0
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load ForecastingCode0
Page time and the order parameter for a consciousness state—0
Reservoir Computing Using Complex Systems—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
Twitter's Agenda-Setting Role: A Study of Twitter Strategy for Political Diversion—0
Construction of a Surrogate Model: Multivariate Time Series Prediction with a Hybrid Model—0
Adaptive Multi-Agent Continuous Learning SystemCode0
Multi-Level Association Rule Mining for Wireless Network Time Series Data—0
Design-time Fashion Popularity Forecasting in VR Environments—0
On LASSO for High Dimensional Predictive Regression—0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Temporal Weights—0
Nonparametric Independent Component Analysis for the Sources with Mixed Spectra—0
On Mini-Batch Training with Varying Length Time SeriesCode0
Improving Accuracy Without Losing Interpretability: A ML Approach for Time Series Forecasting—0
Land use/land cover dynamics on vulnerable regions in Uruguay approached by a method combining Maximum Entropy and Population Dynamics—0
POPNASv3: a Pareto-Optimal Neural Architecture Search Solution for Image and Time Series Classification—0
Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series—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
Neural Continuous-Time Markov Models—0
Online Real-time Learning of Dynamical Systems from Noisy Streaming Data: A Koopman Operator Approach—0
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period—0
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series—0
Towards Better Long-range Time Series Forecasting using Generative Forecasting—0
Learning State Transition Rules from Hidden Layers of Restricted Boltzmann Machines—0
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