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 3301–3350 of 6748 papers

TitleStatusHype
Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning—0
A machine learning approach to itinerary-level booking prediction in competitive airline markets—0
Interpretable Feature Construction for Time Series Extrinsic Regression—0
Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme DiscoveryCode0
A novel weighted approach for time series forecasting based on visibility graph—0
Anticipating synchronization with machine learning—0
Visualising Deep Network's Time-Series Representations—0
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events—0
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation—0
Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning—0
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach—0
Machine Learning Prediction of Time-Varying Rayleigh Channels—0
Extension of the Lagrange multiplier test for error cross-section independence to large panels with non normal errors—0
Streaming Linear System Identification with Reverse Experience Replay—0
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial FibrillationCode0
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks—0
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
Generating Reliable Process Event Streams and Time Series Data based on Neural NetworksCode0
Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks—0
Classification and Feature Transformation with Fuzzy Cognitive MapsCode0
Optimizing Expected Shortfall under an _1 constraint -- an analytic approach—0
Artificial neural network as a universal model of nonlinear dynamical systems—0
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management—0
Deep Hedging, Generative Adversarial Networks, and Beyond—0
Prediction of financial time series using LSTM and data denoising methods—0
On the LRD of the Aggregated Traffic Flows in High-Speed Computer Networks—0
Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise—0
District Wise Price Forecasting of Wheat in Pakistan using Deep Learning—0
Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models—0
Exploring the representativeness of the M5 competition data—0
Modeling Macroeconomic Variations After COVID-19—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment—0
Two-Stage Framework for Seasonal Time Series Forecasting—0
Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention—0
Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?Code0
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLCode0
Theory of Low Frequency Contamination from Nonstationarity and Misspecification: Consequences for HAR Inference—0
Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series—0
A Spectral Enabled GAN for Time Series Data Generation—0
Variance Reduced Training with Stratified Sampling for Forecasting Models—0
A Novel CNN-LSTM-based Approach to Predict Urban Expansion—0
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
Dynamic and Thermodynamic Models of Adaptation—0
Automatic Stockpile Volume Monitoring using Multi-view Stereo from SkySat Imagery—0
Automated data-driven approach for gap filling in the time series using evolutionary learningCode0
Identifying the module structure of swarms using a new framework of network-based time series clustering—0
The Kernel Trick for Nonlinear Factor Modeling—0
CLPVG: Circular limited penetrable visibility graph as a new network model for time series—0
Unsupervised dynamic modeling of medical image transformationCode0
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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