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

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Showing 4951–5000 of 6748 papers

TitleStatusHype
Efficient Kernel-based Subsequence Search for User Identification from Walking Activity—0
Extending Deep Learning Models for Limit Order Books to Quantile Regression—0
Trip Table Estimation and Prediction for Dynamic Traffic Assignment Applications—0
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions—0
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend FilteringCode0
A Statistical Recurrent Stochastic Volatility Model for Stock Markets—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-SeriesCode0
ZeLiC and ZeChipC: Time Series Interpolation Methods for Lebesgue or Event-based Sampling—0
Energy Predictive Models with Limited Data using Transfer Learning—0
Evolution of Hierarchical Structure & Reuse in iGEM Synthetic DNA Sequences—0
Application of Machine Learning to accidents detection at directional drilling—0
Mutual Information and the Edge of Chaos in Reservoir Computers—0
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian—0
Neural Learning of Online Consumer Credit Risk—0
CCMI : Classifier based Conditional Mutual Information EstimationCode0
PI-Net: A Deep Learning Approach to Extract Topological Persistence ImagesCode0
Streaming Variational Monte CarloCode0
Manifold-regression to predict from MEG/EEG brain signals without source modelingCode0
Automatic Health Problem Detection from Gait Videos Using Deep Neural NetworksCode0
A Fast-Optimal Guaranteed Algorithm For Learning Sub-Interval Relationships in Time Series—0
Learning Interpretable Shapelets for Time Series Classification through Adversarial Regularization—0
Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA—0
Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of SnapchatCode0
Context Dependent Semantic Parsing over Temporally Structured Data—0
Super-resolution of Time-series Labels for Bootstrapped Event Detection—0
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes—0
Patch LearningCode0
Learning low-dimensional state embeddings and metastable clusters from time series data—0
Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate—0
Multimodal Transformer for Unaligned Multimodal Language SequencesCode0
Using Clinical Notes for ICU Management—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
On Policy Evaluation with Aggregate Time-Series Shocks—0
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends—0
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting—0
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes—0
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series—0
General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in DrosophilaCode0
Efficient Covariance Estimation from Temporal DataCode0
Dissection of Bitcoin's Multiscale Bubble History from January 2012 to February 2018—0
Monotonic Gaussian Process FlowCode0
Learning the Non-linearity in Convolutional Neural Networks—0
Learning Temporal Causal Sequence Relationships from Real-Time Time-Series—0
Deep Factors for Forecasting—0
Graph-based era segmentation of international financial integration—0
Evaluating time series forecasting models: An empirical study on performance estimation methodsCode0
BreizhCrops: A Time Series Dataset for Crop Type MappingCode0
Exploring Interpretable LSTM Neural Networks over Multi-Variable DataCode0
ODE^2VAE: Deep generative second order ODEs with Bayesian neural networksCode0
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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