Giới thiệu đến Dự báo Dự đoán các sự kiện trong tương lai và điều kiện được gọi là dự báo, hành động làm cho dự đoán được gọi là dự báo. Dự báo là rất quan trọng trong nhiều tổ chức kể từ khi dự đoán các sự kiện trong tương lai có thể cần phải được kết hợp trong quá trình ra quyết định. Họ cũng cần thiết để đưa ra quyết định thông minh. Một trường đại học phải có khả năng dự báo nhập học của học sinh để đưa ra các quyết định liên quan. | Chapter 7 Predictors Introduction to Forecasting Predictions of future events and conditions are called forecasts the act of making predictions is called forecasting. Forecasting is very important in many organizations since predictions of future events may need to be incorporated in the decisionmaking process. They are also necessary in order to make intelligent decisions. A university must be able to forecast student enrollment in order to make decisions concerning faculty resources and housing availability. In forecasting events that will occur in the future a forecaster must rely on information concerning events that have occurred in the past. That is why the forecasters must analyze past data and must rely on this information to make a decision. The past data is analyzed in order to identify a pattern that can be used to describe it. Then the pattern is extrapolated or extended to forecast future events. This basic strategy is employed in most forecasting techniques rest on the assumption that a pattern that has been identified will continue in the future. Time series are used to prepare forecasts. They are chronological sequences of observations of a particular variable. Time series are often examined in hopes of discovering a historical pattern that can be exploited in the preparation of a forecast. An example is shown in Table . Table Data for forecasting example Time s Current mA P. Ponce-Cruz F. D. Ramirez-Figueroa Intelligent Control Systems with LabVIEW Springer 2010 191 192 7 Predictors A time series is a composition of several components in order to identify patterns 1. Trend. Refers to the upward or downward movement that characterizes a time series over a period of time. In other words it reflects the long-run growth or decline in the time series. 2. Cycle. Recurring up and down movements around trend levels. 3. Seasonal variations. Periodic patterns in time series that complete themselves within
