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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 2701–2750 of 6748 papers

TitleStatusHype
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data—0
Explaining Time Series Predictions with Dynamic MasksCode1
Deep Learning Statistical ArbitrageCode1
Parameter Inference with Bifurcation DiagramsCode1
RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting—0
Time-series Imputation of Temporally-occluded Multiagent Trajectories—0
Manifold Topology Divergence: a Framework for Comparing Data ManifoldsCode1
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting—0
Differentiable Multiple Shooting Layers—0
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic ForecastingCode1
The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurvesCode0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Deep Particulate Matter Forecasting Model Using Correntropy-Induced LossCode0
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
Latent Time-Adaptive Drift-Diffusion Model—0
Deep Switching State Space Model (DS^3M) for Nonlinear Time Series Forecasting with Regime SwitchingCode1
Price graphs: Utilizing the structural information of financial time series for stock predictionCode1
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
A General Method for Event Detection on Social Media—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
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems—0
Homological Time Series Analysis of Sensor Signals from Power PlantsCode0
Causal Digital Twin from Multi-channel IoT—0
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic RegressionCode1
MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled TrajectoriesCode0
Deep Signature Statistics for Likelihood-free Time-series Models—0
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes—0
Warming up recurrent neural networks to maximise reachable multistability greatly improves learning—0
Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural NetworksCode0
Quantifying the Effects of COVID-19 on Restaurant Reviews—0
Unsupervised Representation Learning for Time Series with Temporal Neighborhood CodingCode1
Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments—0
Fast, Accurate and Interpretable Time Series Classification Through RandomizationCode1
Pattern Discovery in Time Series with Byte Pair Encoding—0
An improved LogNNet classifier for IoT application—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
Asset volatility forecasting:The optimal decay parameter in the EWMA model—0
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline SummarizationCode0
Accelerating Neural ODEs Using Model Order Reduction—0
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty QuantificationCode1
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy OptimizationCode0
Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint DetectionCode1
Short-Term Stock Price-Trend Prediction Using Meta-Learning—0
Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State—0
Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—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