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An improved fuzzy time-series method of forecasting based on L-R fuzzy sets and its application

机译：基于L-R模糊集的改进的模糊时间序列预测方法及其应用

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摘要

Classical time-series theory assumes values of the response variable to be 'crisp' or 'precise', which is quite often violated in reality. However, forecasting of such data can be carried out through fuzzy time-series analysis. This article presents an improved method of forecasting based on L-R fuzzy sets as membership functions. As an illustration, the methodology is employed for forecasting India's total foodgrain production. For the data under consideration, superiority of proposed method over other competing methods is demonstrated in respect of modelling and forecasting on the basis of mean square error and average relative error criteria. Finally, out-of-sample forecasts are also obtained.
机译：古典时间序列理论假定响应变量的值为“精确”或“精确”，这在现实中经常被违反。但是，可以通过模糊时间序列分析对此类数据进行预测。本文提出了一种基于L-R模糊集作为隶属函数的改进的预测方法。作为说明，该方法用于预测印度的粮食总产量。对于所考虑的数据，在均方误差和平均相对误差标准的基础上，相对于其他竞争方法，该方法在建模和预测方面具有优势。最后，还获得了样本外预测。

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