def arimaPredict():
# 首先创建类实例
ap = arimaPredicter.predicter()
# 设置索引，函数的第一个参数是训练数据开始的日期，第二次参数是索引的长度，索引
# 长度不小于训练数据的长度即可
ap.createIndex(dt.datetime(2015, 1, 1), 243)
# 可以直接调用sarimaTrain函数训练arima模型，只需将训练标签输入即可
model = ap.sarimaTrain(trainLabel)
# 得到模型后调用sarimaPredict函数便可以预测紧接着训练数据之后若干天的预测值，
# 两个参数分别为先前得到的模型与预测序列的长度
# 这是一个静态函数，可以直接通过类名调用
predictLabel = ap.sarimaPredict(model, 43)
# 这样便可以得到结果
plt.plot(testLabel, color='blue', label='actual')
plt.plot(predictLabel, color='red', label='predict')
plt.title('ARIMA(default)')
plt.show(block=False)
# 事实上，在sarimaTrain函数中，你也可以指定ARIMA模型的两个参数（ar, ma）
model = ap.sarimaTrain(trainLabel, para=(2, 2))
# 如果参数指定得当，结果将更好，反之更糟糕
predictLabel = ap.sarimaPredict(model, 43)
plt.plot(testLabel, color='blue', label='actual')
plt.plot(predictLabel, color='red', label='predict')
plt.title('ARIMA(2, 2)')
plt.show(block=False)
# 如果你不知道该指定什么参数，那么可以使用sarimaParaSelect函数选择参数，该函数
# 的输入为类别名称，训练集，测试集及决定在参数选择时是否参考AIC的布尔变量
# 目前来看，在参数选择时是否参考AIC的结果差不多
# 函数执行后将会返回最优的参数以及测试集上的运行结果，同时实例中也会以类别名称为
# 键存储这个最优参数
para, _ = ap.sarimaParaSelect(1001, trainLabel[:-50], trainLabel[-50:],
True)
# 由于最优参数已被存储，再次训练是指明类别名称即可
model = ap.sarimaTrain(trainLabel, classNo=1001)
# 预测的方式始终相同
predictLabel = ap.sarimaPredict(model, 43)
plt.plot(testLabel, color='blue', label='actual')
plt.plot(predictLabel, color='red', label='predict')
plt.title('ARIMA(%d, %d)' % (para[0], para[1]))
plt.show(block=False)
def sariamTest():
f = open("datam.csv", "r")
f_csv = csv.reader(f)
# writer = open("report.txt", "w")
ap = arimaPredicter.predicter()
ap.setIndex(index)
for i in range(0, 10):
midclass, trD, trL, teD, teL = getData(f_csv, 120, 0)
if (midclass == 0):
break
trainData = trL[:99]
testData = trL[99:]
ap.test_stationarity(trL)
greatfit = (0, 0, 0)
minaic = 99999
for p in range(0, 3):
for q in range(0, 3):
try:
ap.setPara(midclass, (p, q))
model = ap.sarimaTrain(midclass, trainData)
if (model.aic < minaic):
minaic = model.aic
greatfit = (p, 1, q)
result = ap.sarimaPredict(model, len(testData))
print("(%d,%d) %f %f\n" %
(p, q, model.aic, getBias(testData, result)))
except:
pass
print("midclass %d: %d %d\n" % (midclass, greatfit[0], greatfit[2]))
f.close()
import KNN_interface
larclasPred = {}
larclasLabl = {}
totalBias = 0
totalCount = 0
dtIndex = [dt.datetime(2015, 1, x) for x in range(1, 32)]
dtIndex = dtIndex + [dt.datetime(2015, 2, x) for x in (range(1, 29))]
dtIndex = dtIndex + [dt.datetime(2015, 3, x) for x in range(1, 32)]
dtIndex = dtIndex + [dt.datetime(2015, 4, x) for x in (range(1, 31))]
modelChoose = []
lcModelChoose = []
ap = arimaPredicter.predicter()
ap.setIndex(dtIndex)
def dataLog(midclass, accuracy, trainLabl, testPred, testLabl):
with open('compare.csv', 'ab') as f:
writer = csv.writer(f)
count = 1
writer.writerow([midclass, accuracy])
for x in trainLabl:
writer.writerow([count, x])
count += 1
for x in range(0, len(testPred)):
writer.writerow([count, testLabl[x], testPred[x]])
count += 1
def sariamOutput():
loader = dataLoader.loader("datam.csv", "lcdatam.csv")
loader.setSize(120, 0, 0)
f1 = open("result01.csv", "wb")
writer1 = csv.writer(f1)
f2 = open("result11.csv", "wb")
writer2 = csv.writer(f2)
f3 = open("result12.csv", "wb")
writer3 = csv.writer(f3)
ap = arimaPredicter.predicter()
ap.setIndex(index)
while (True):
midclass, _, trainData, _, _ = loader.getNextMidClass()
if (midclass == 0):
break
ap.setPara(midclass, (0, 1))
try:
model = ap.sarimaTrain(midclass, trainData)
result = ap.sarimaPredict(model, 30)
except:
result = np.zeros(30)
for i in range(0, 30):
writer1.writerow([midclass, "201505%02d" % (i + 1), result[i]])
ap.setPara(midclass, (1, 1))
try:
model = ap.sarimaTrain(midclass, trainData)
result = ap.sarimaPredict(model, 30)
except:
result = np.zeros(30)
for i in range(0, 30):
writer2.writerow([midclass, "201505%02d" % (i + 1), result[i]])
ap.setPara(midclass, (1, 2))
try:
model = ap.sarimaTrain(midclass, trainData)
result = ap.sarimaPredict(model, 30)
except:
result = np.zeros(30)
for i in range(0, 30):
writer3.writerow([midclass, "201505%02d" % (i + 1), result[i]])
while (True):
larclass, _, trainData, _, _ = loader.getNextLarClass()
if (larclass == 0):
break
ap.setPara(larclass, (0, 1))
try:
model = ap.sarimaTrain(larclass, trainData)
result = ap.sarimaPredict(model, 30)
except:
result = np.zeros(30)
for i in range(0, 30):
writer1.writerow([larclass, "201505%02d" % (i + 1), result[i]])
ap.setPara(larclass, (1, 1))
try:
model = ap.sarimaTrain(larclass, trainData)
result = ap.sarimaPredict(model, 30)
except:
result = np.zeros(30)
for i in range(0, 30):
writer2.writerow([larclass, "201505%02d" % (i + 1), result[i]])
ap.setPara(larclass, (1, 2))
try:
model = ap.sarimaTrain(larclass, trainData)
result = ap.sarimaPredict(model, 30)
except:
result = np.zeros(30)
for i in range(0, 30):
writer3.writerow([larclass, "201505%02d" % (i + 1), result[i]])
f1.close()
f2.close()
f3.close()
loader.closeFiles()