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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 3051–3100 of 6748 papers

TitleStatusHype
EuroCrops: A Pan-European Dataset for Time Series Crop Type Classification—0
Dynamic Asymmetric Causality Tests with an Application—0
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images—0
Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion ArtifactsCode0
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings—0
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions—0
Multivariate Pair Trading by Volatility & Model Adaption Trade-off—0
Generative Adversarial Networks in finance: an overview—0
Probability Paths and the Structure of Predictions over TimeCode0
Time Series Anomaly Detection with label-free Model Selection—0
A new measure between sets of probability distributions with applications to erratic financial behavior—0
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties—0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands—0
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series DataCode0
Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos—0
Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks—0
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data—0
Time-series Imputation of Temporally-occluded Multiagent Trajectories—0
RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting—0
Differentiable Multiple Shooting Layers—0
The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurvesCode0
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting—0
Deep Particulate Matter Forecasting Model Using Correntropy-Induced LossCode0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Fast and Robust Online Inference with Stochastic Gradient Descent via Random ScalingCode0
Distributed Learning and its Application for Time-Series Prediction—0
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition—0
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
Latent Time-Adaptive Drift-Diffusion Model—0
A General Method for Event Detection on Social Media—0
Homological Time Series Analysis of Sensor Signals from Power PlantsCode0
MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled TrajectoriesCode0
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems—0
The relationship between the US broad money supply and US GDP for the time period 2001 to 2019 with that of the corresponding time series for US national property and stock market indices, using an information entropy methodology—0
Causal Digital Twin from Multi-channel IoT—0
Warming up recurrent neural networks to maximise reachable multistability greatly improves learning—0
Deep Signature Statistics for Likelihood-free Time-series Models—0
Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural NetworksCode0
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes—0
Quantifying the Effects of COVID-19 on Restaurant Reviews—0
Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments—0
An improved LogNNet classifier for IoT application—0
Pattern Discovery in Time Series with Byte Pair Encoding—0
Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing—0
Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data—0
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline SummarizationCode0
Asset volatility forecasting:The optimal decay parameter in the EWMA model—0
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy OptimizationCode0
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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