Application Of Time-series Data Research Articles

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering.

Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research.

Exploration of ecological environment construction and development paths in towns and cities by applying time series data

Abstract This paper explores the interaction mechanism between urbanization and the ecological environment based on the unique correlation between urbanization and the ecological environment. BISE and Savizky-Golay algorithms are combined to establish the time series data, and for the characteristic curves of the EVI time response of the land cover, the spectral angle mapping method is combined with the minimum distance method to form the spectral angle-minimum distance classification method. The application of time series data involves calculating the time and spatial change of urban land cover and analyzing the evolution of urban ecological environment elements. Based on the temporal and spatial consistency test and land cover analysis of urban ecological environment construction, as well as the development of urbanization construction, relevant development paths are proposed. The comprehensive score of new urbanization in the four districts and one city of Hengyang City is concentrated towards 1.6, indicating that the level of economic development and the construction of new urbanization have reached coupling at different levels, respectively.

Implementation of Naïve bayes Method for Predictor Prevalence Level for Malnutrition Toddlers in Magelang City

Nutritional status is an important factor in assessing the growth and development rate of babies and toddlers. Cases of malnutrition are increasing, especially in magelang city. Because nutritional problems (Malnutrition) can affect the health of toddlers. Therefore, this study aims to predict the level of prevalence of malnutrition with the Naïve Bayes method. This research uses an observational design, a single center study at the Magelang City Office, using the Naïve bayes method which is used as an application of time series data, and is most widely used for prediction, especially in data sets that have many categorical or nominal type attributes. The Naïve bayes method is used to predict such cases of malnutrition. The results of this study show that the Naïve Bayes method succeeded in predicting the magnitude of cases of malnourished toddlers in Magelang City with an accuracy percentage of 75% due to the very minimal amount of training data, and the areas that have the most malnutrition are in three areas, namely Magersari, North Tidar and Panjang.

Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

Spectral density estimation with amplitude modulation and outlier detection

This paper studies spectral density estimation based on amplitude modulation including missing data as a specific case. A generalized periodogram is introduced and smoothed to give a consistent estimator of the spectral density by running local linear regression smoother. We explore the asymptotic properties of the proposed estimator and its application to time series data with periodic missing. A simple data-driven local bandwidth selection rule is proposed and an algorithm for computing the spectral density estimate is presented. The effectiveness of the proposed method is demonstrated using simulations. The application to outlier detection based on leave-one-out diagnostic is also considered. An illustrative example shows that the proposed diagnostic procedure succeeds in revealing outliers in time series without masking and smearing effects.

Econometric critique of the economic change model of mortality

The application of time-series data and analysis to study the effects of changes in unemployment rates on mortality rates has been a controversial issue in health-unemployment research for many years. This article presents new criticism against previous aggregate time-series regression models and concludes that these models are misspecified in functional form, and the t-ratios used in significance tests are grossly overstated. Future empirical analysis of the Economic Change Model of Mortality, i.e. the aggregate, time-series relationship between mortality rates and economic variables must pay more attention to the salient characteristics of time-series data and implications for regression results.