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 1451–1475 of 6748 papers

TitleStatusHype
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series ForecastingCode1
Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots—0
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics—0
Dependent Latent Class ModelsCode0
Forecasting Solar Power Generation on the basis of Predictive and Corrective Maintenance Activities—0
Automated Mobility Context Detection with Inertial Signals—0
Multi-scale Attention Flow for Probabilistic Time Series Forecasting—0
Joint cardiac T_1 mapping and cardiac function estimation using a deep manifold framework—0
A Data Cube of Big Satellite Image Time-Series for Agriculture MonitoringCode0
Towards Space-to-Ground Data Availability for Agriculture MonitoringCode1
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNsCode0
HARNet: A Convolutional Neural Network for Realized Volatility ForecastingCode1
Statistical Modeling and Forecasting of Automatic Generation Control Signals—0
Market-Based Asset Price Probability—0
Nonparametric Value-at-Risk via Sieve EstimationCode0
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks—0
Modelling stellar activity with Gaussian process regression networksCode0
Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training—0
Method of indirect estimation of default probability dynamics for industry-target segments according to the data of Bank of Russia—0
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
Efficient Automated Deep Learning for Time Series ForecastingCode4
An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics—0
Characterization of electric consumers through an automated clustering pipeline—0
Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs—0
Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death ProcessesCode0
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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