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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3251–3300 of 6748 papers

TitleStatusHype
Forecast with Forecasts: Diversity Matters—0
ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling—0
Complex systems: features, similarity and connectivity—0
Increasing Server Availability for Overall System Security: A Preventive Maintenance Approach Based on Failure Prediction—0
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models—0
Independence clustering (without a matrix)—0
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process—0
Indexing the Event Calculus with Kd-trees to Monitor Diabetes—0
Indian Economy and Nighttime Lights—0
Indirect Measurement of Hepatic Drug Clearance by Fitting Dynamical Models—0
Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior—0
Individual Topology Structure of Eye Movement Trajectories—0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—0
Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM—0
Complex systems approach to natural language—0
A Review on Deep Learning in UAV Remote Sensing—0
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed—0
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks—0
Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network—0
Inference in heavy-tailed non-stationary multivariate time series—0
Complex market dynamics in the light of random matrix theory—0
Inference in Non-stationary High-Dimensional VARs—0
Inference of Binary Regime Models with Jump Discontinuities—0
Inference of Causal Information Flow in Collective Animal Behavior—0
Inference of High-dimensional Autoregressive Generalized Linear Models—0
Inference of stochastic time series with missing data—0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Inference on the change point in high dimensional time series models via plug in least squares—0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
INFERENCE, PREDICTION, AND ENTROPY RATE OF CONTINUOUS-TIME, DISCRETE-EVENT PROCESSES—0
Inferential Theory for Granular Instrumental Variables in High Dimensions—0
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes—0
Inferring, comparing and exploring ecological networks from time-series data through R packages constructnet, disgraph and dynet—0
Complexity Measures and Features for Times Series classification—0
A Review of Wind Speed and Wind Power Forecasting Techniques—0
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies—0
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks—0
Inferring Global Dynamics Using a Learning Machine—0
Forecasting with a Panel Tobit Model—0
Inferring Individual Level Causal Models from Graph-based Relational Time Series—0
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization—0
Complexity-based Financial Stress Evaluation—0
Forecasting under Long Memory and Nonstationarity—0
Forecasting trends with asset prices—0
Complexity and Persistence of Price Time Series of the European Electricity Spot Market—0
Inferring the time-varying functional connectivity of large-scale computer networks from emitted events—0
Infinite-Dimensional Adaptive Boundary Observer for Inner-Domain Temperature Estimation of 3D Electrosurgical Processes using Surface Thermography Sensing—0
Infinite Mixture Model of Markov Chains—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—0
Forecasting Time Series with VARMA Recursions on Graphs—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