Talk · International Conference on Problems of Cybernetics and Informatics · August 28, 2023

Time Series Analysis of Biogas Monitoring with Deep Learning Approaches

Vladislav Rudakov, Timur Merembayev, Amirgaliyev Yedilkhan, Перизат Омарова

Abstract

Time series data, which comprises a sequence of timestamps and distinct values, is widely utilized in various industrial sectors to monitor systems. Due to the considerable amount of accumulated data and the requirement for operational analysis, a novel form of database has emerged: Time series databases created specifically for the storage and retrieval of time series data. The aim of this investigation is to introduce a compilation of well-liked time series databases and a comprehensive list of characteristics for open source databases. The investigation yielded a total of 4 databases. As a result, 5 criteria have been established, namely: the speed of reading and writing, the size of the database, the support for time-series table, and the convenience of the API. Additionally, the paper delves into the significance of data processing in intelligent technical device monitoring systems, encompassing data extraction, transformation, and loading, event detection, and visualization.

Time Series Analysis and ForecastingAdvanced Database Systems and QueriesEnvironmental Monitoring and Data Managementtime series databasesbiogas monitoringdeep learning

Time Series Analysis of Biogas Monitoring with Deep Learning Approaches

Abstract

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