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 3701–3750 of 6748 papers

TitleStatusHype
Benchmarking adversarial attacks and defenses for time-series data—0
Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression—0
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution LearningCode0
Fast Partial Fourier Transform—0
Koopman Mode Decomposition of Oscillatory Temperature Field inside a Room—0
Learning Compact Physics-Aware Delayed Photocurrent Models Using Dynamic Mode Decomposition—0
Forecasting with Multiple SeasonalityCode0
DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data—0
Investigation of Flash Crash via Topological Data Analysis—0
Gesture Recognition from Skeleton Data for Intuitive Human-Machine Interaction—0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Automated Model Selection for Time-Series Anomaly Detection—0
Photonic reservoir computer based on frequency multiplexing—0
Prediction of Hilbertian autoregressive processes : a Recurrent Neural Network approach—0
Comparative Computational Analysis of Global Structure in Canonical, Non-Canonical and Non-Literary Texts—0
Counterfactual Explanations for Machine Learning on Multivariate Time Series DataCode1
ATM Cash demand forecasting in an Indian Bank with chaos and deep learning—0
USAD: UnSupervised Anomaly Detection on Multivariate Time SeriesCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction—0
Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial NetworksCode1
Evaluating Machine Learning Models for the Fast Identification of Contingency Cases—0
The Canonical Interval Forest (CIF) Classifier for Time Series Classification—0
Exact Tests for Offline Changepoint Detection in Multichannel Binary and Count Data with Application to Networks—0
Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting—0
Reforming the State-Based Forward Guidance through Wage Growth Rate Threshold: Evidence from FRB/US SimulationsCode0
Augmenting Neural Differential Equations to Model Unknown Dynamical Systems with Incomplete State InformationCode0
MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting—0
Long vs Short Time Scales: the Rough Dilemma and Beyond—0
Generative Adversarial Networks for Spatio-temporal Data: A Survey—0
Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks—0
RTFN: Robust Temporal Feature Network—0
Predicting Future Sales of Retail Products using Machine Learning—0
A Formally Robust Time Series Distance Metric—0
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting—0
Extension of causal decomposition in the mutual complex dynamic process—0
Deep Neural Networks for automatic extraction of features in time series satellite images—0
HiPPO: Recurrent Memory with Optimal Polynomial ProjectionsCode1
Learning from Irregularly-Sampled Time Series: A Missing Data PerspectiveCode1
Wavelet Denoising and Attention-based RNN-ARIMA Model to Predict Forex Price—0
Gait complexity assessed by detrended fluctuation analysis is sensitive to inconsistencies in stride time series: A modeling study—0
Neural Network-based Automatic Factor Construction—0
Image Processing Tools for Financial Time Series Classification—0
Spectral Processing of COVID-19 Time-Series DataCode0
A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models—0
Learning low-frequency temporal patterns for quantitative tradingCode0
Decoding kinetic features of hand motor preparation from single‐trial EEG using convolutional 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