samp中的ARIMA外生变量

2024-05-20 00:55:05 发布

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fit = statsmodels.api.tsa.ARIMA(efRates[0], (1,1,1), exog=ueRate).fit(transparams=False)

predicts = fit.predict(start=len(efRates[0]), end = len(efRates[0])+11, exog=ueRate, typ = 'levels')

生成

^{pr2}$

一些信息:efRates[0]和{}是相同长度的列表。在

efRates[0] [0.030052056971642007, 0.03917330288542586, 0.02828475062426216, 0.03644101079605235, 0.03378605359919436, 0.02743587918046455, 0.03342745492501596, 0.026205917483282503, 0.030503758568976337, 0.024550760529053202, 0.03261189266424876, 0.03506521240864593, 0.027338276601998696, 0.053725765854704746, 0.02676967429100413, 0.03442977438269886, 0.033314687425925964, 0.027406120117972988, 0.037085495711527916, 0.021131004053371122, 0.03342530957311805, 0.02011467948214261, 0.03674645825546184, 0.030766279328527657, 0.022010347634637235, 0.048441932020847935, 0.055182794314502556, 0.037653187998947804, 0.054329400023020905, 0.030487014172364307, 0.04828703019272537, 0.029364609341652963, 0.04420916320116292, 0.0245732204143899, 0.04007219462688283, 0.030088483595491378, 0.04503547974992547, 0.050414257448672777, 0.03650945820093438, 0.0271939590858418, 0.043825558271225154, 0.02887263694287208, 0.034395655516300985, 0.033476222069816444, 0.02364138126589003, 0.034956784469719566, 0.025488157761323762, 0.03284135171594629, 0.0352266773873871, 0.02578522887525815, 0.030801158226067212, 0.017836011389627614, 0.03237266466197845, 0.020781381627205192, 0.03507981277516531, 0.030619701683938114, 0.0200645972051283, 0.02340543468851082, 0.022232375406303732, 0.031450255120488005, 0.030807264010862326, 0.02520300632649576, 0.02683432106844716, 0.01719544921035768, 0.022245308176032028, 0.015787396423808154, 0.02236691164709978, 0.022948859956318242, 0.018302596298743336, 0.02356268219722402, 0.020514907102090335, 0.029322000183361653, 0.030253386469667742, 0.02389996663574461, 0.026350732450672106, 0.018634569853141162, 0.02993859530565429, 0.01762489169698181, 0.028369112029450066, 0.024207088908217232, 0.019513438046869554, 0.02149236584384482, 0.020792834468107983, 0.0252767276304043, 0.025754940371044845, 0.01633653635317383, 0.02562719118582408, 0.01718720874173012, 0.02915438356543398, 0.017238835380189263, 0.028044663751279383, 0.027504015027686957, 0.020989801458819447, 0.025215885766374995, 0.02422123160263125, 0.03253702270430853, 0.02095284431753602, 0.03241141468118923, 0.018667854534336364, 0.03997670839216877, 0.022116655885610726, 0.030336876645878957, 0.03418820217137176, 0.018663800522544426, 0.02623414798030232, 0.020524065760586897]

ueRate [4.9, 5, 5, 5, 4.9, 4.7, 4.8, 4.7, 4.7, 4.6, 4.6, 4.7, 4.7, 4.5, 4.4, 4.5, 4.4, 4.6, 4.5, 4.4, 4.5, 4.4, 4.6, 4.7, 4.6, 4.7, 4.7, 4.7, 5, 5, 4.9, 5.1, 5, 5.4, 5.6, 5.8, 6.1, 6.1, 6.5, 6.8, 7.3, 7.8, 8.3, 8.7, 9, 9.4, 9.5, 9.5, 9.6, 9.8, 10, 9.9, 9.9, 9.7, 9.8, 9.9, 9.9, 9.6, 9.4, 9.5, 9.5, 9.5, 9.5, 9.8, 9.4, 9.1, 9, 9, 9.1, 9, 9.1, 9, 9, 9, 8.8, 8.6, 8.5, 8.2, 8.3, 8.2, 8.2, 8.2, 8.2, 8.2, 8.1, 7.8, 7.8, 7.8, 7.9, 7.9, 7.7, 7.5, 7.5, 7.5, 7.5, 7.3, 7.2, 7.2, 7.2, 7, 6.7, 6.6, 6.7, 6.7, 6.3, 6.3]


Tags: apifalselenstartpredictfitendstatsmodels