Beyond Integration: A Symmetry-Based Approach to Reaching Stationarity in Economic Time Series

© Springer Nature Switzerland AG 2020. Many efficient data processing techniques assume that the corresponding process is stationary. However, in areas like economics, most processes are not stationery: with the exception of stagnation periods, economies usually grow. A known way to apply stationari...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Songsak Sriboonchitta, Olga Kosheleva, Vladik Kreinovich
التنسيق: Book Series
منشور في: 2020
الموضوعات:
الوصول للمادة أونلاين:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85080865050&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68336
الوسوم: إضافة وسم
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المؤسسة: Chiang Mai University
الوصف
الملخص:© Springer Nature Switzerland AG 2020. Many efficient data processing techniques assume that the corresponding process is stationary. However, in areas like economics, most processes are not stationery: with the exception of stagnation periods, economies usually grow. A known way to apply stationarity-based methods to such processes—integration—is based on the fact that often, while the process itself is not stationary, its first or second differences are stationary. This idea works when the trend polynomially depends on time. In practice, the trend is usually non-polynomial: it is often exponentially growing, with cycles added. In this paper, we show how integration techniques can be expanded to such trends.