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引用次数: 4
摘要
时间序列预测是通过对历史数据与未来数据相互关联的假设研究进行的。移动平均线(MA)是一种众所周知的预测方法。虽然已经有了软计算和修正方法等几种方法,但传统的移动平均方法由于其简单,在最近的研究中仍然得到了应用。本研究开发了基于web的Phatsa (PHP application for Time Series Analysis)应用程序,该应用程序实现了三种传统的移动平均线方法,即SMA、WMA和EMA。所有的计算都在100-5000行时间序列数据集中进行测试,最大计算时间在0.4秒以下。计算时间与其方程的复杂度是相对线性的,EMA的时间最多,SMA的时间最少。
Phatsa: A web-based application for forecasting using conventional moving average methods
Forecasting with time series was made by hypothetical study that historical data related with the future data. Moving average (MA) is one of the well-known methods to be used in forecasting technique. Although there are several methods that already developed like soft computing and modified methods, conventional moving average methods still implemented in recent researches because of its simplicity. This study conducts a development of web-based application called Phatsa (PHP Application for Time Series Analysis) that implements three conventional moving average methods, i.e. the SMA, WMA and EMA. All the calculation tested in 100–5000 rows of time series dataset with the maximum calculating time below 0.4 seconds. Calculating time is relatively linear with the complexity of its equation, EMA has the biggest amount of time while SMA has the lowest one.