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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2701–2725 of 6748 papers

TitleStatusHype
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data—0
Explaining Time Series Predictions with Dynamic MasksCode1
Parameter Inference with Bifurcation DiagramsCode1
Deep Learning Statistical ArbitrageCode1
Time-series Imputation of Temporally-occluded Multiagent Trajectories—0
Manifold Topology Divergence: a Framework for Comparing Data ManifoldsCode1
RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting—0
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
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
A General Method for Event Detection on Social Media—0
Price graphs: Utilizing the structural information of financial time series for stock predictionCode1
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
Causal Digital Twin from Multi-channel IoT—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