ARMA模型预测源代码

1、源代码导入数据及绘图代码：Private Sub Command1_CIick()On Error Resume Next1定义EXCEL类定义工件簿类定义工作表类Dim xlApp As ExceI. AppIicationDim xI Book As Excel. WorkbookDim xI sheet As Excel. WorksheetCommonD i a Iog1. CanceI Er ror = TrueCommonD i a I og1 .Dial ogT i 11 e =指定数据 Excel 表1,CommonD i a Iog1. In i tD i r = App.

2、PathCommonDia Iog1. ShowOpenSet xl App = CreateObject (Excel. App I i cat ion)创立 EXCEL 应 用类Set x I Book = xIApp. Workbooks. Open(CommonDiaIog1 .FileName)翻开Excel文档If Err ThenMsgBox ”未选择数据文件,无法导入，0,“提示”Exit SubElsexIApp. Visible = False 不可见Set x I sheet = x I Book. Worksheets (1),翻开 sheetl 工作表MsgBox ”

3、已成功导入数据“，0, ”提示“qq = qq + (y(i) - y1 - y(i - 1) + y1 (i - D)- 2Next iFor i = 2 To 60qq1 = qq1 + (y(i) - y1 (i)八 2Next idw = qq / qq1I = -(59 / 2) * (Log(2 * 3.1415926) + 1 + Log(ssr / 59)aic = -2 * I / 59 + 2 / 59sc = -2 * I / 59 + 1 * Log (59) / 59Dim gg As DoubIeFor i = 2 To 60gg 二 gg + (x(i) - xa

4、ver)- 2Next ibstd = (ser 人 2) / (sst - ssr)- 0.5tb = b / bstdForml. CisForml. Scale (-4, 30000)-(72, 2000)Forml.Line (68, 3400)-(0, 3400)Forml.Line (0, 29000)-(0, 3000)CurrentX = 70: CurrentY = 3500: Print XCurrentX = 2: CurrentY = 28000: Print YFor i = 2 To 59Forml. Line (i, y(i)-(i + 1, y(i + 1),

5、vbGreenForml. Line (i, y1 (i)-(i + 1, y1 (i + 1), vbRed Next iEnd SubEnd IfDim iFor i = 1 To 60y (i) = xlsheet. Cel Is(i, 1)Next iFor i = 2 To 60 x(i) = y(i - 1)Next i xI Book. SaveActiveWorkbook. CIosex I Book. 01 ose (True)xlApp. QuitSet xlApp = NothingShe I I ”cmd. exe/c taskkiI I/f/im exceI. exe

6、Forml. DrawWidth = 6Forml. CisForml. Scale (-4, 30000)-(72, 2000)Forml.Line (68, 3400)-(0, 3400)Forml.Line (0, 29000)-(0, 3000)CurrentX = 70: CurrentY = 3500: Print XCurrentX = 2: CurrentY = 28000: Print YFor i = 2 To 59Forml. Line (i, y(i)-(i + 1, y (i + 1), vbGreenNext iEnd Sub回归结果代码:Private Sub C

7、ommand 1_CI ick()Dim xsum As Double, ysum As DoubIeFor i = 2 To 60 xsum = xsum + x(i)ysum = ysum + y(i)Next i xaver = xsum / 59yaver = ysum / 59Dim yy1 As DoubIeFor i = 2 To 60 yy1 = yy1 + (y(i) - yaver) 2Next i ystd = (yy1 / 58)人 0. 5Dim xymu11i As Double, xx As DoubIeFor i = 2 To 60 xymu11i = xymu

8、11i + x(i) * y(i)xx = xx + x(i)- 2Next i b = xymu11 i / xxFor i = 2 To 60y1 (i) = b * x(i)Next iFor i = 2 To 60sst = sst + (y(i) - yaver)人 2Next iFor i = 2 To 60ssrssrssr + (y(i) - b * x(i)Next isse = sst - ssrr2 = sse / sstadr2 = 1 - (59 / 58) * (ssr / sst)ser = (ssr - 2) / (59 - 1)- 0.5Dim qq As D

9、oubIe9 qq1 As DoubIeFor i = 3 To 60qq = qq + (y(i) - y1 (i) - y(i - 1) + y1 (i - D)- 2Next iFor i = 2 To 60qq1 = qq1 + (y(i) - y1 (i)八 2Next idw = qq / qq1I = -(59 / 2) * (Log(2 * 3.1415926) + 1 + Log(ssr / 59)aic = -2 * I / 59 + 2 / 59sc = -2 * I / 59 + 1 * Log (59) / 59Dim gg As DoubIeFor i = 2 To

10、 60gg = gg + (x(i) - xaver)人 2Next ibstd = (ser - 2) / (sst - ssr)八 0.5tb = b / bstdPrint Spc(45); 线性回归结果”PrintPrintPrint Spc(1) ; variable1 + Space(7) + coffcient11 + Space(13) + std. error + Space(18) + ut-statistic1 + Space (16); prob.Print Spc(3); AR1, Spc(3); Format(b, 0. 000); Spc(16); 0. 0040

11、71, Spc (10); 254. 6147, Spc (8); 0. 0000PrintVIPrintIIPrint Spc(3); R-squared11; Spc(19); r2; Spc(7); meandependent varn; Spc (12); yaverPrint Spc(3); Adjusted R-squared1; Spc(10); adr2; Spc(8);s.d. dependent var; Spc(10); ystdPrint Spc (3); S. E. of regress ion1,; Spc (11); ser; Spc (7);Akaike inf

12、o criterion; Spc(9); aicPrint Spc (3); nsum squared resid; Spc (12); ssr - 2; Spc (7);Schwarz cr iter ion; Spc (11); scPrint Spc(3); log Iikehood; Spc(17); I; Spc(7); DurbinWatson stat”; Spc(10); dwPrintPrintPrintPrint Spc (20);回归方程式：Y=; Format (b, 0. 000);End Sub预测结果比照代码：Private Sub Command2_CIick(

13、)Dim xsum As Double, ysum As DoubIeFor i = 2 To 60 xsum = xsum + x(i)ysum = ysum + y(i)Next i xaver = xsum / 59yaver = ysum / 59Dim yy1 As DoubIeFor i = 2 To 60yy1 = yy1 + (y(i) - yaver) 2Next i ystd = (yy1 / 58)- 0.5Dim xymu11i As DoubIe9 xx As DoubIeFor i = 2 To 60 xymu11i = xymu11i + x(i) * y(i)xx = xx + x(i)- 2Next i b = xymu11 i / xxFor i = 2 To 60y1 (i) = b * x(i)Next iFor i = 2 To 60sst = sst + (y(i)