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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3201–3250 of 6748 papers

TitleStatusHype
Improved Grey System Models for Predicting Traffic Parameters—0
Improved Modeling of Complex Systems Using Hybrid Physics/Machine Learning/Stochastic Models—0
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification—0
Improved PAC-Bayesian Bounds for Linear Regression—0
Improved Prediction and Network Estimation Using the Monotone Single Index Multi-variate Autoregressive Model—0
Improved Predictive Deep Temporal Neural Networks with Trend Filtering—0
Composite FORCE learning of chaotic echo state networks for time-series prediction—0
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)—0
Improvement of Flood Extent Representation with Remote Sensing Data and Data Assimilation—0
A review on outlier/anomaly detection in time series data—0
Forgery Attack Detection in Surveillance Video Streams Using Wi-Fi Channel State Information—0
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis—0
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks—0
Forestry digital twin with machine learning in Landsat 7 data—0
Composable Generative Models—0
Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features—0
Foreign exchange risk premia: from traditional to state-space analyses—0
Foreign Exchange Market Performance: Evidence from Bivariate Time Series Approach—0
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition—0
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning—0
Improving Irregularly Sampled Time Series Learning with Dense Descriptors of Time—0
Complex-valued Gaussian Process Regression for Time Series Analysis—0
Improving MF-DFA model with applications in precious metals market—0
A review on distance based time series classification—0
A Method for Massively Parallel Analysis of Time Series—0
Improving Optimization for Models With Continuous Symmetry Breaking—0
A Data-Driven Approach for Modeling Stochasticity in Oil Market—0
Forecast with Forecasts: Diversity Matters—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
Improving Robustness on Seasonality-Heavy Multivariate Time Series Anomaly Detection—0
ForecastTB An R Package as a Test-Bench for Time Series Forecasting Application of Wind Speed and Solar Radiation Modeling—0
Improving Solar Flare Prediction by Time Series Outlier Detection—0
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary—0
Complex systems: features, similarity and connectivity—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility—0
Improving the quality control of seismic data through active learning—0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data—0
Improving the spectral resolution of fMRI signals through the temporal de-correlation approach—0
Improving the Thermal Infrared Monitoring of Volcanoes: A Deep Learning Approach for Intermittent Image Series—0
Improving Time Series Classification Algorithms Using Octave-Convolutional Layers—0
Complex systems approach to natural language—0
Forecasting, Causality, and Impulse Response with Neural Vector Autoregressions—0
A Review on Deep Learning in UAV Remote Sensing—0
Complex market dynamics in the light of random matrix theory—0
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic—0
Complexity Measures and Features for Times Series classification—0
A Review of Wind Speed and Wind Power Forecasting Techniques—0
A Method for Estimating the Entropy of Time Series Using Artificial 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