def test(data, lookBack, epoch, lr, batchSize, method, modelPath):
# 归一化数据
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(data)
# 分割序列为样本, 支持RNN或者普通样本形式
trainData, testData = divideTrainTest(dataset)
flag = True
trainX, trainY = createSamples(trainData, lookBack, RNN=flag)
testX, testY = createSamples(testData, lookBack, RNN=flag)
print("testX shape:", testX.shape)
print("testy shape:", testY.shape)
print("trainX shape:", trainX.shape)
print("trainy shape:", trainY.shape)
train(trainX,
trainY,
epoch=epoch,
lr=lr,
batchSize=batchSize,
modelPath=modelPath,
lookBack=lookBack,
method=method)
testPred = predict(testX, MODEL_PATH)
trainPred = predict(trainX, MODEL_PATH)
print("testPred shape:", testPred.shape)
print("trainPred shape:", trainPred.shape)
testPred = scaler.inverse_transform(testPred)
testY = scaler.inverse_transform(testY)
MAE = eval.calcMAE(testY, testPred)
print("test MAE", MAE)
MRSE = eval.calcRMSE(testY, testPred)
print("test RMSE", MRSE)
# MAPE = eval.calcMAPE(testY, testPred)
# print("test MAPE", MAPE)
SMAPE = eval.calcSMAPE(testY, testPred)
print("test SMAPE", SMAPE)
return trainPred, testPred, MAE, MRSE, SMAPE
def testSVM(data, lookBack):
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(data)
# 分割序列为样本
trainData, testData = divideTrainTest(dataset)
flag = False
trainX, trainY = createSamples(trainData, lookBack, RNN=flag)
testX, testY = createSamples(testData, lookBack, RNN=flag)
print("testX shape:", testX.shape)
print("testy shape:", testY.shape)
print("trainX shape:", trainX.shape)
print("trainy shape:", trainY.shape)
model = trainSVM(trainX, trainY)
testPred = predictSVM(testX, model)
trainPred = predictSVM(trainX, model)
print("testPred shape:", testPred.shape)
print("trainPred shape:", trainPred.shape)
testPred = scaler.inverse_transform(testPred)
testY = scaler.inverse_transform(testY)
MAE = eval.calcMAE(testY, testPred)
print("test MAE", MAE)
MRSE = eval.calcRMSE(testY, testPred)
print("test RMSE", MRSE)
# MAPE = eval.calcMAPE(testY, testPred)
# print("test MAPE", MAPE)
SMAPE = eval.calcSMAPE(testY, testPred)
print("test SMAPE", SMAPE)
return trainPred, testPred, MAE, MRSE, SMAPE
def FCD_Train(ts, dataset, freq, lookBack, batchSize, epoch, lr, method):
# 序列分解
#ts.index = pd.date_range(start='19960318',periods=len(ts), freq='Q')
trend, seasonal, residual = seasonDecompose(ts, freq)
print("fcd decompose is finised!")
print("trend shape is", trend.shape)
print("season shape is", seasonal.shape)
print("residual shape is", residual.shape)
# 分别预测
MODEL_PATH = "../model/ResRNN_model.pkl"
# trainPred, testPred, MAE, MRSE, SMAPE = test(data=dataset, lookBack=lag, epoch=epoch,
# batchSize=batchSize, method=method, modelPath=MODEL_PATH)
trTrain, trTest, MAE1, MRSE1, SMAPE1 = test(trend,
lookBack,
epoch,
lr,
batchSize,
method=method,
modelPath=MODEL_PATH)
resTrain, resTest, MAE2, MRSE2, SMAPE2 = test(residual,
lookBack,
epoch,
lr,
batchSize,
method=method,
modelPath=MODEL_PATH)
# trTrain, trTest, MAE1, MRSE1, SMAPE1= RNNFORECAST.RNNforecasting(trend, lookBack=resWin, epoch=30, unit=unit,
# varFlag=True, minLen=20, maxLen=lag, step=4,
# hiddenNum=100)
# resTrain, resTest, MAE2, MRSE2, SMAPE2 = RNNFORECAST.RNNforecasting(residual, lookBack=resWin, epoch=30, unit=unit,
# varFlag=True, minLen=20, maxLen=lag, step=4, hiddenNum=100)
trTrain = trTrain.reshape(-1)
trTest = trTest.reshape(-1)
resTrain = resTrain.reshape(-1)
resTest = resTest.reshape(-1)
print("trTrain shape is", trTrain.shape)
print("resTrain shape is", resTrain.shape)
# '''
# 数据对齐
trendPred, resPred = align(trTrain, trTest, lookBack, resTrain, resTest,
lookBack)
print("trendPred shape is", trendPred.shape)
print("resPred shape is", resPred.shape)
# 获取最终预测结果
finalPred = trendPred + seasonal + resPred
trainPred = trTrain + seasonal[lookBack:lookBack +
trTrain.shape[0]] + resTrain
testPred = trTest + seasonal[2 * lookBack + resTrain.shape[0]:] + resTest
# 获得ground-truth数据
data = dataset[freq // 2:-(freq // 2)]
trainY = data[lookBack:lookBack + trTrain.shape[0]]
testY = data[2 * lookBack + resTrain.shape[0]:]
print(trainY.shape)
print(testY.shape)
print(trainPred.shape)
print(testPred.shape)
# 评估指标
MAE = eval.calcMAE(trainY, trainPred)
print("train MAE", MAE)
MRSE = eval.calcRMSE(trainY, trainPred)
print("train MRSE", MRSE)
MAPE = eval.calcMAPE(trainY, trainPred)
print("train MAPE", MAPE)
MAE = eval.calcMAE(testY, testPred)
print("test MAE", MAE)
MRSE = eval.calcRMSE(testY, testPred)
print("test RMSE", MRSE)
MAPE = eval.calcMAPE(testY, testPred)
print("test MAPE", MAPE)
SMAPE = eval.calcSMAPE(testY, testPred)
print("test SMAPE", SMAPE)
# plt.plot(data)
# plt.plot(finalPred)
# plt.show()
# '''
return trainPred, testPred, MAE, MRSE, SMAPE
def FCD_Train_SVM(ts, dataset, freq, lookBack):
# 序列分解
#ts.index = pd.date_range(start='19960318',periods=len(ts), freq='Q')
trend, seasonal, residual = seasonDecompose(ts, freq)
print("fcd decompose is finised!")
print("trend shape is", trend.shape)
print("season shape is", seasonal.shape)
print("residual shape is", residual.shape)
# 分别预测
trTrain, trTest, MAE1, MRSE1, SMAPE1 = testSVM(trend, lookBack)
resTrain, resTest, MAE2, MRSE2, SMAPE2 = testSVM(residual, lookBack)
trTrain = trTrain.reshape(-1)
trTest = trTest.reshape(-1)
resTrain = resTrain.reshape(-1)
resTest = resTest.reshape(-1)
print("trTrain shape is", trTrain.shape)
print("resTrain shape is", resTrain.shape)
# 数据对齐
trendPred, resPred = align(trTrain, trTest, lookBack, resTrain, resTest,
lookBack)
print("trendPred shape is", trendPred.shape)
print("resPred shape is", resPred.shape)
# 获取最终预测结果
finalPred = trendPred + seasonal + resPred
trainPred = trTrain + seasonal[lookBack:lookBack +
trTrain.shape[0]] + resTrain
testPred = trTest + seasonal[2 * lookBack + resTrain.shape[0]:] + resTest
# 获得ground-truth数据
data = dataset[freq // 2:-(freq // 2)]
trainY = data[lookBack:lookBack + trTrain.shape[0]]
testY = data[2 * lookBack + resTrain.shape[0]:]
print(trainY.shape)
print(testY.shape)
print(trainPred.shape)
print(testPred.shape)
# 评估指标
MAE = eval.calcMAE(trainY, trainPred)
print("train MAE", MAE)
MRSE = eval.calcRMSE(trainY, trainPred)
print("train MRSE", MRSE)
MAPE = eval.calcMAPE(trainY, trainPred)
print("train MAPE", MAPE)
MAE = eval.calcMAE(testY, testPred)
print("test MAE", MAE)
MRSE = eval.calcRMSE(testY, testPred)
print("test RMSE", MRSE)
MAPE = eval.calcMAPE(testY, testPred)
print("test MAPE", MAPE)
SMAPE = eval.calcSMAPE(testY, testPred)
print("test SMAPE", SMAPE)
# plt.plot(data)
# plt.plot(finalPred)
# plt.show()
# '''
return trainPred, testPred, MAE, MRSE, SMAPE
# print(order.bic_min_order)
#model = ARIMA(history, order=(3, 2, 1))
model = pf.ARIMA(data=np.array(history), ar=4, ma=4, family=pf.Normal())
model.fit(method="MLE")
output = model.predict(1, intervals=False)
yhat = output.values[0][0]
predictions.append(yhat)
obs = test[t]
history.append(obs)
realTestY.append(obs)
print('predicted=%f, expected=%f' % (yhat, obs))
realTestY = np.array(test)
predictions = np.array(predictions).reshape(-1)
#print(realTestY)
print(predictions)
MAE = eval.calcMAE(realTestY,predictions)
RMSE = eval.calcRMSE(realTestY,predictions)
MAPE = eval.calcSMAPE(realTestY,predictions)
print ('Test MAE: %.8f' % MAE)
print ('Test RMSE: %.8f' % RMSE)
print ('Test MAPE: %.8f' % MAPE)
# plot
# pyplot.plot(test)
# pyplot.plot(predictions, color='red')
# pyplot.show()