def decompose_RNN_forecasting(ts, dataset, freq, lag, epoch=20, hidden_num=64,
batch_size=32, lr=1e-3, unit="GRU", varFlag=False, maxLen=48, minLen=24, step=8):
# 序列分解
trend, seasonal, residual = decompose.ts_decompose(ts, freq)
print("trend shape:", trend.shape)
print("peroid shape:", seasonal.shape)
print("residual shape:", residual.shape)
# 分别预测
resWin = trendWin = lag
t1 = time.time()
trTrain, trTest, MAE1, MRSE1, SMAPE1 = RNN_forecasting(trend, lookBack=lag, epoch=epoch, batchSize=batch_size, hiddenNum=hidden_num,
varFlag=varFlag, minLen=minLen, maxLen=maxLen, step=step, unit=unit, lr=lr)
resTrain, resTest, MAE2, MRSE2, SMAPE2 = RNN_forecasting(residual, lookBack=lag, epoch=epoch, batchSize=batch_size, hiddenNum=hidden_num,
varFlag=varFlag, minLen=minLen, maxLen=maxLen, step=step, unit=unit, lr=lr)
t2 = time.time()
print(t2-t1)
print("trTrain shape:", trTrain.shape)
print("resTrain shape:", resTrain.shape)
# 数据对齐
trendPred, resPred = util.align(trTrain, trTest, trendWin, resTrain, resTest, resWin)
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[trendWin:trendWin+trTrain.shape[0]]+resTrain
testPred = trTest+seasonal[2*resWin+resTrain.shape[0]:]+resTest
# 获得ground-truth数据
data = dataset[freq//2:-(freq//2)]
trainY = data[trendWin:trendWin+trTrain.shape[0]]
testY = data[2*resWin+resTrain.shape[0]:]
# 评估指标
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(testY, label='ground-truth')
plt.plot(testPred, label='prediction')
plt.xlabel("Time", fontsize=10)
plt.ylabel("CPU Utilization(%)", fontsize=10)
plt.legend()
foo_fig = plt.gcf()
foo_fig.savefig('M_1955_CPU.eps', format='eps', dpi=1000, bbox_inches='tight')
plt.show()
return trainPred, testPred, MAE, MRSE, SMAPE
def decompose_MLP_forecasting(ts,
dataset,
freq,
lag,
epoch=20,
hidden_num=64,
batch_size=32,
lr=1e-3):
# 序列分解
trend, seasonal, residual = decompose.ts_decompose(ts, freq=freq)
print("trend shape:", trend.shape)
print("peroid shape:", seasonal.shape)
print("residual shape:", residual.shape)
# 分别预测
resWin = trendWin = lag
t1 = time.time()
trTrain, trTest, mae1, mrse1, smape1 = \
ANNFORECAST.MLP_forecasting(trend, inputDim=trendWin, epoch=epoch, hiddenNum=hidden_num,
batchSize=batch_size, lr=lr)
resTrain, resTest, mae2, mrse2, smape2 = \
ANNFORECAST.MLP_forecasting(residual, inputDim=trendWin, epoch=epoch, hiddenNum=hidden_num,
batchSize=batch_size, lr=lr)
t2 = time.time()
print("time:", t2 - t1)
# 数据对齐
# trendPred, resPred = util.align(trTrain, trTest, trendWin, resTrain, resTest, resWin)
# 获取最终预测结果
# finalPred = trendPred + seasonal + resPred
trainPred = trTrain + seasonal[trendWin:trendWin +
trTrain.shape[0]] + resTrain
testPred = trTest + seasonal[2 * resWin + resTrain.shape[0]:] + resTest
# 获得ground-truth数据
data = dataset[freq // 2:-(freq // 2)]
trainY = data[trendWin:trendWin + trTrain.shape[0]]
testY = data[2 * resWin + resTrain.shape[0]:]
# 评估指标
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 decompose_SVR_forecasting(ts, dataset, freq, lag, C=0.1, epsilon=0.01):
# 序列分解
trend, seasonal, residual = decompose.ts_decompose(ts, freq=freq)
print("trend shape:", trend.shape)
print("peroid shape:", seasonal.shape)
print("residual shape:", residual.shape)
# 分别预测
resWin = trendWin = lag
t1 = time.time()
trTrain, trTest, mae1, mrse1, mape1 = SVR_forecasting(trend,
lookBack=lag,
C=C,
epsilon=epsilon)
resTrain, resTest, mae2, mrse2, mape2 = SVR_forecasting(residual,
lookBack=lag,
C=C,
epsilon=epsilon)
t2 = time.time()
print(t2 - t1)
# 数据对齐
trendPred, resPred = util.align(trTrain, trTest, trendWin, resTrain,
resTest, resWin)
# 获取最终预测结果
finalPred = trendPred + seasonal + resPred
trainPred = trTrain + seasonal[trendWin:trendWin +
trTrain.shape[0]] + resTrain
testPred = trTest + seasonal[2 * resWin + resTrain.shape[0]:] + resTest
# 获得ground-truth数据
data = dataset[freq // 2:-(freq // 2)]
trainY = data[trendWin:trendWin + trTrain.shape[0]]
testY = data[2 * resWin + resTrain.shape[0]:]
# 评估指标
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