1. Time sequence

In the prediction of the development prospects of the residential energy storage system, a series of economic indicators that change according to time, such as the sale of the company's energy storage system products by the year (quarter)

Sales, supply volume, etc., a set of data arranged by time is called time sequence. The method of prediction according to time sequence is called time sequence prediction.

2. Return

(1) The meaning of "regression". Return refers to the dependence between analyzing and studying a variable (due to variables) and one or several other variables (independent variables). The purpose is to estimate or predict the cause of the cause based on the data values of a known authentic variable data. The overall average of variables. In economic predictions, people take prediction objects (economic indicators) as due to variables, and those influencing factors that are closely related to prediction objects are used as independent variables. According to the historical and statistical data of the two, the regression model is established, and it is used for prediction after statistical inspection. The regression forecast has a one -dollar regression forecast of an independent variable and a variety of regression forecasts of multiple independent variables. Here we only discuss the one -dollar linear regression forecast method.

(2) Basic conditions of regression analysis. When applying a group of known autonomous variable data to estimate and predict one value of one due to variables, these two variables need to meet the following two conditions:

First, statistical correlation. Statistical correlation is an uncertain functional relationship, that is, the value of a value of one or more independent variables is obviously related but cannot be accurate and cannot be uniquely determined. It is a random variable. This correlation in economic phenomena exists in large quantities.

Second, cause and effect. If one or more independent variable x changes, the other variable y is affected by a certain law, and the change of Y cannot affect X, that is, the change of x is the reason for the change of y Cause relationships, the model that reflects the cause and effect is called the regression model.