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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 2276–2300 of 6748 papers

TitleStatusHype
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium PropagationCode0
Classification of Time-Series Data Using Boosted Decision TreesCode0
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time SeriesCode1
Uncertainty, volatility and the persistence norms of financial time series—0
Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series—0
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series—0
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values—0
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 MethodsCode0
Multi Scale Graph Wavenet for Wind Speed Forecasting—0
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Deep Neural Networks on EEG signals to predict Attention Score using Gramian Angular Difference Field—0
Long-term Prediction of Nonlinear Time Series Using Autoencoder and Echo State Networks—0
An Optimally Weighted Echo State Neural Network for Highly Chaotic Time Series Modelling—0
TransTCN: An Attention-based TCN Framework for Sequential Modeling—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
Less is more: Selecting the right benchmarking set of data for time series classification—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization—0
Tractable Dendritic RNNs for Identifying Unknown Nonlinear Dynamical Systems—0
Multi-Task Processes—0
Objective Evaluation of Deep Visual Interpretations on Time Series Data—0
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis—0
Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series—0
A Study of Aggregation of Long Time-series Input for LSTM Neural Networks—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