2007/01/30 by Koushik Ghosh, Ghosh, Koushik, Pratap Raychaudhuri +2
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Advanced Text Analysis Techniques #Algorithm #Artificial intelligence #Astrophysics (astro-ph) #Computer science #Data mining #Data science #FOS: Physical sciences #Forecasting Techniques and Applications #Geology #Machine learning #Mathematics #Noise (video) #Random error #Random noise #Series (stratigraphy) #Statistics #Systematic error #TRACE (psycholinguistics) #Time Series Analysis and Forecasting #Time series #Tracing #astro-ph
paper · pdf · doi:10.48550/arxiv.astro-ph/0701863
published in arXiv (Cornell University) (Cornell University) · 4 pages
arxiv created 2007/01/30 · openalex publication_date 2007/01/30 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A physical (e.g. astrophysical, geophysical, meteorological etc.) data may appear as an output of an experiment or it may contain some sociological, economic or biological information. Whatever be the source of a time series data some amount of noise is always expected to be embedded in it. Analysis of such data in presence of noise may often fail to give accurate information. Although text book data filtering theory is primarily concerned with the presences of random, zero mean errors; but in reality, errors in data are often systematic rather than random. In the present paper we produce different models of systematic error in the time series data. This will certainly help to trace the systematic error present in the data and consequently that can be removed as possible to make the data compatible for further study.