非平稳时间序列数据的过差分与预测

Z. Hossain, Atikur Rahman, Moyazzem Hossain, J. Karami
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引用次数: 10

摘要

在时间序列分析中,过度差分是使数据趋于平稳的一种常见现象。然而,为了保证时间序列数据的平稳性,采用过度差分并不总是一个好主意。本文通过仿真研究,考察了过度差分的影响,观察拟合模型与真实模型的距离。仿真结果表明,在本文考虑的大多数情景中,由于过度差异,拟合模型与真实模型存在很大差异。在实际应用中,可能值得考虑对时间序列数据进行差分和适当的变换,使其平稳。对月平均房价的非平稳时间序列数据,采用变换和差分相结合的方法,保证其平稳。然后我们分析数据并对未来的值进行预测。达卡大学学报:自然科学版,67(1):21-26,2019 (1)
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Over-Differencing and Forecasting with Non-Stationary Time Series Data
In time series analysis, over-differencing is a common phenomenon to make the data to be stationary. However, it is not always a good idea to take over-differencing in order to ensure the stationarity of time series data. In this paper, the effect of over-differencing has been investigated via a simulation study to observe how far or how close the fitted model from the true one. Simulation results show that the fitted model is found to be different and very far from the true model because of over-differencing in most of the scenarios considered in this study. In practice, it may be worthy to consider differencing as well as suitable transformation of the time series data to make it stationary. Both transformation and differencing are used for a non-stationary time series data on average monthly house prices to ensure it to be stationary. We then analyse the data and make prediction for the future values. Dhaka Univ. J. Sci. 67(1): 21-26, 2019 (January)
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