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 4551–4575 of 6748 papers

TitleStatusHype
Analysis of Hydrological and Suspended Sediment Events from Mad River Watershed using Multivariate Time Series Clustering—0
Application of Time Series Analysis to Traffic Accidents in Los Angeles—0
Topological Machine Learning for Multivariate Time SeriesCode0
A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers—0
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data—0
A Time Series Analysis of Emotional Loading in Central Bank Statements—0
Universal EEG Encoder for Learning Diverse Intelligent Tasks—0
Network Intrusion Detection based on LSTM and Feature Embedding—0
An Optimized and Energy-Efficient Parallel Implementation of Non-Iteratively Trained Recurrent Neural Networks—0
Revisiting Deep Architectures for Head Motion Prediction in 360° Videos—0
TimeCaps: Capturing Time Series Data With Capsule NetworksCode0
Correlative Channel-Aware Fusion for Multi-View Time Series Classification—0
High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration—0
Functional Bayesian Filter—0
Differentiable Algorithm for Marginalising Changepoints—0
Modeling emotion in complex stories: the Stanford Emotional Narratives DatasetCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values—0
Deep Reinforcement Learning for Trading—0
Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition—0
On the separation of shape and temporal patterns in time series -Application to signature authentication-Code0
Time Series Classification: Lessons Learned in the (Literal) Field while Studying Chicken Behavior—0
Multi-Scale RCNN Model for Financial Time-series Classification—0
Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints—0
A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM—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