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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 6301–6325 of 6748 papers

TitleStatusHype
Clustering Financial Time Series: How Long is Enough?—0
Real time error detection in metal arc welding process using Artificial Neural Netwroks—0
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies—0
Support Vector Machines with Time Series Distance Kernels for Action ClassificationCode0
Coordination Event Detection and Initiator Identification in Time Series DataCode0
Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding—0
A Langevin model for complex cardiological time series—0
Clustering Based Feature Learning on Variable StarsCode0
Modified Profile Likelihood Inference and Interval Forecast of the Burst of Financial Bubbles—0
Seeking for a fingerprint: analysis of point processes in actigraphy recording—0
Causal Discovery from Subsampled Time Series Data by Constraint Optimization—0
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series—0
Parsimonious modeling with Information Filtering Networks—0
Lagged and instantaneous dynamical influences related to brain structural connectivity—0
Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems—0
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM—0
Statistical modeling of isoform splicing dynamics from RNA-seq time series data—0
Solar energy production: Short-term forecasting and risk management—0
Scaling up Dynamic Topic ModelsCode0
Learning Over Long Time Lags—0
Machine olfaction using time scattering of sensor multiresolution graphs—0
Chaos in Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic Processes—0
Convolutional Radio Modulation Recognition NetworksCode1
Lasso Guarantees for Time Series Estimation Under Subgaussian Tails and β-Mixing—0
The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version—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