Python + facebook Prophet 预测错误

Question

告诉我我做错了什么。我在预言家输入中插入一个时间序列，我得到了一个预测，但它看起来像一个重复的模式。完全不像预测的那样。

import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
from sktime.forecasting.fbprophet import Prophet

salesData = [-22.899810546632665, -9.11228684600458, -2.0232803049199948, 2.769880807257286, 0.8771850621655833, 5.240523426843543, -4.101202313494994, -7.438606008637052, 1.8640765345461658, 2.6530910373074446, 0.6816397761862772, -5.369005299189235, 2.2995963745863106, -0.9488515556078191, -2.490658867190924, 1.495849663486175, 1.849161620028351, 4.574290696478775, 1.8606830281445728, 4.5401410593181915, -5.0697333665656465, -6.8037280937002205, -0.41728360333518366, -0.397468128796101, 1.0964987155515669, 0.9241856998562122, 7.4987636157925674, -0.9036386621033988, -4.168575486734736, 1.0455838313276498, 3.5501944037387263, 3.7117838928135645, -2.249350543191892, -0.9325974026874418, -7.311336798694246, -4.769395107147262, 1.7712018973129169, 2.6453558795159933, 4.805405561414102, 3.8260210836580826, 4.394563377865766, -7.139777583888209, -5.2122838464550005, 3.4707574766029285, 2.123455819765237, -1.9216781708796522, -2.696474264481818, 4.3072928655137765, -4.835464310939693, -4.715567254042179, 4.837730825402849, 7.923534727836371, 2.584596049852443, -3.485498284318281, -0.1855356912460123, -6.521909243801553, -5.939170879395363, 1.6440237896855472, 3.580429853920485, 1.3774941555516405, -0.9196574985857207, 3.992221788802156, -1.935957074886787, -3.7997988733436756, 2.714021017101176, 3.0294525494024, -0.3930365150839221, -5.009292419867927, 0.7546979885019088, -2.1174967380732563, -4.788564073800437, -0.952874211429072, 2.781963488012231, 7.9459681649254525, 3.629590909086231, 1.7861643533664724, -4.624868314831825, -3.520074030081029, 0.6087172369876066, -1.1062737995516618, 2.6359835833191347, 3.3113477448178408, 1.689695851822031, -7.095504239394035, -3.5249810225744573, 6.588101250291994, 4.085385013734247, 1.4832832692866167, 0.9734299151513489, 2.6112162346070504, -4.9010306775769275, -4.901239447552297, 3.8950095820646213, 1.3406292538018294, -0.5282837546993199, 0.753952323998906, 5.169079271848939, -1.6201860123291287, -3.762162418130858, 4.275051800352548, 1.1232101108209884, -2.346202168861502, -2.2826782569255832, 4.505890767755019, -1.8190665385734426, -7.658329819004368, 0.10987851344719599, 2.261897124488089, 1.5392501079425294, -1.5844040229997323, 3.683259856560565, 1.1829387289652118, -4.237938986869985, -1.8026666795474242, -1.7946250217271775, 1.2933788146545167, -1.0374578898470987, 0.3434342927014619, -1.7379029412348141, -2.7776369281015727, 1.6505959323578854, -0.9845160970786264, 2.73600663050934, 1.912088176390693, 1.5064291044360094, -4.185993981551647, -5.078603135175087, 1.580810068159558, -1.827676946096123, 1.630245976939615, 0.8767970006001486, 1.9191902082102914, -4.255257065345466, -2.2920106230775206, 6.452410589766132, 1.490241277720836, 0.29745142343220105, -2.022758354575668, 1.166445503203743, -5.432583501507066, -3.2137062880925975, 5.964950921188283, 2.647583725388037, -0.5602763181540208, -2.4389785201658296, 4.185419628541755, -0.47753222108873544, -1.7125950465824034, 2.7545209513686912, 0.342690874987082, -1.1297987908423226, -2.044073816608947, 3.2448098431419643, -1.3658253244506777, -3.0147128514534787, 1.4737794066696859, 2.1205697423773944, 3.048455865920152, 1.3545170452380675, 2.10067165714669, -3.793222879838366, -4.987808008915057, 0.5774951459683085, 1.8218849657934184, 3.307490703034141, 1.9780212087919815, 1.017752319426859, -3.987452442233335, -3.233130377966213, 3.114846702211204, 1.700873563247121, 1.4460065798295234, 1.2663580286331433, 0.587237520548546, -5.66622533912726, -6.23057647388975, 2.149189925604655, 2.414391072519844, 2.2696711137534034, 2.1172763760340816, 1.9510409807356304, -3.7813490807218475, -5.93040981385517, 1.4416758657765874, 1.2545519774333878, 1.9386196629746348, 1.2256240869780195, 0.43855005180747997, -3.8095930731545153, -4.143332375838307, 3.7552822457456716, 1.3420669360588162, 1.3597044716717293, 1.33624713051985, 2.379028078399799, -2.399594563350425, -4.541685080886469, 1.4735219894517995, -1.4039630434543284, 0.66682849702559, 2.245620397491483, 4.4033376300176785, -1.0964722703121415, -4.076951580495554, 1.291026487846525, -1.0855622042189113, 2.021799003901511, 3.3846547403984886, 3.6166899070710716, -4.452017017323524, -5.947523546504931, 2.727763154202456, 1.5435457414281373, 1.9410359497085359, 1.1448645515499383, 2.511180115230774, -3.4770991100961717, -4.9485301299371995, 2.936927151150847, 1.2839461841529514, -0.07087615474581482, -2.8211352474973266, -0.22166213045039357, -1.6727202982802507, -2.4620856585437685, 2.9576801111982873, -1.0473392914946011, 0.18190414813076905, -1.2105705320239823, 0.9254667533052756, -1.9219262740652698, -3.1279930246559458, 1.895358870235939, -2.3953798117147485, 0.6985464824740497, -0.7965075353915932, 1.9591944951226141, -2.517142588893044, -1.676673849478618, 2.84604398527469, -1.0894803442635672, 1.4783835295440593, -0.0555177047161724, 1.6056866092191024, -4.77750003577151, -1.544584491913734, 3.107665546950072, -0.006254614425190333, 0.22423083652751807, 1.7005777930167314, 4.063346193604185, -3.6485240780514268, -2.3458028072183454, 2.520911245490198, 1.9419752563460058, 0.3787820835300505, 2.0498064393412077, 3.147056229388338, -4.013310003373203, -4.362542880984366, 1.5791861684970858, 4.335046506280617, 3.024238669831118, 2.6683220831269496, 0.31176370249733526, -5.618366178783114, -5.267583273223407, 1.7326529550861156, 4.382096829624017, 2.0455805167051846, 1.0366320874674322, -1.9173315713803762, -4.0357688468957615, -2.8014441825258554, 2.565601196897678, 2.2515635294504164, 0.38499715498107095, 2.2908723588858173, -0.8979113432652993, -2.4615590649006482, -4.036513305578183, 1.1582182715691045, -0.8529716577272682, 1.0479873029226106, 6.53669448953843, 2.0355434398513306, -2.617144384775319, -8.06489443517519, 1.3207354479678062, -0.8506484224720485, 3.5640871478901244, 4.775311871095769, 1.147305365017473, -4.285285935325304, -7.629241354464176, 4.545770169990173, -0.027498715864489598, 4.46282927184866, 1.4592336747437304, 3.3706422928017794, -3.3689345604601484, -4.565641812297128, 3.1252066027077245, -2.4158556661884467, 2.37788801634457, 1.1416295182577751, 7.36482011925607, -2.76449323711107, -4.7092182478193525, -1.07666105787791, -1.0243511859113654, 4.565943078068873, 2.2147757486814985, 3.865577422352626, -7.230372724333538, -3.8398148291606216, 0.9939239884760955, 1.4333479713928579, 2.5779115150194096, 0.8221690914339992, 2.4098682578136152, -6.629744793568462, -1.5110744533130211, 2.156484249766369, 1.523300217711056, -1.1811913084632777, 0.4090603117126238, 3.3805594527547562, -4.322160577084377, -1.9905151201929976, -0.870049065627476, 1.5971712874405182, -0.28835577991007966, 3.543064453803165, 2.4565420116791223, -6.427468387594416, -5.704961705197211, -2.3371090366067686, 3.566469474778068, 1.9648586802921713, 6.430843208262392, -2.0029942672566983, -8.421587742473228, -6.414213371184237, 2.6665441248117823, 7.065714062786268, 1.4156562691223726, 3.688760151381901, -6.627341388200704, -3.2205813402830885, -1.8073613922283878, 5.3895839529841405, -1.2095943247915644, -1.1067957899354275, 7.444579017378822, 1.610983987241812, -0.5864043533909686, -6.676970411917687, 2.521362831867282, -5.1450562775781705, 5.7475117965483085, 8.371715838155241, 1.9229179356836248, -7.628853886388433]

#convert data to prophet format input
salesData = [np.float64(item) for item in salesData]
test_date = datetime.datetime.strptime("2022-01-01", "%Y-%m-%d")
#generating dates list
listOfDates = pd.date_range(test_date, periods=364)
#compile series for Prophet input
ddf = pd.Series(salesData,index=listOfDates[:365])

#Prophet begin
forecaster = Prophet(seasonality_mode='multiplicative',
                     weekly_seasonality=True
                     )
forecaster.fit(ddf)
y_pred = forecaster.predict(fh=range(0,50))
plt.plot(range(0,144),salesData[:144], label='Original')
plt.plot(range(144,194),y_pred, label='Prophet forecast')
plt.legend(loc='best')
plt.show()

Answer 1

欢迎安德烈来到 Stackoverflow。我会给你一个详细的答案。

1. 您的时间序列数据看起来没有针对预测任务进行预处理。为什么要手动设置数据？尝试稍微清理一下并额外查看一下第一个数据点是否从 -22 开始出现异常。尝试标准化或重新调整数据。

2. Prophet 不是您想要使用的预测模型，原因有几个。但非理论的简短答案是：如果你不知道 Prophet 的基本机制，它就很糟糕。首先尝试 ARIMA 模型，例如 AR、ARMA、ARIMA、SARIMA 或 ETS。

3. 您的设置是

   multiplicative

   ，但您的时间序列不显示乘法模式。我想说它是累加的，因为它不会随着时间的推移而增加。但你似乎对季节性一无所知。对我来说，它看起来像是一个随机的嘈杂时间序列。运行没有任何季节性。

最后。如果数据不合适，任何模型都无法帮助您。