decompose_SVR_forecasting.py

import util
from models import decompose
import eval
from naive_SVR_forecasting import SVR_forecasting
import time


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


if __name__ == "__main__":

    lag = 24
    freq = 4
    C = 0.01
    epsilon = 0.01

    # ts, data = util.load_data("./data/NSW2013.csv", columnName="TOTALDEMAND")
    # ts, data = util.load_data("./data/bike_hour.csv", columnName="cnt")
    # ts, data = util.load_data("./data/TAS2016.csv", columnName="TOTALDEMAND")
    # ts, data = util.load_data("./data/traffic_data_in_bits.csv", columnName="value")
    ts, data = util.load_data("./data/beijing_pm25.csv", columnName="pm2.5")
    # ts, data = util.load_data("./data/pollution.csv", columnName="Ozone")

    trainPred, testPred, mae, mrse, smape = decompose_SVR_forecasting(ts, data, lag=lag, freq=freq,
                                                                      C=C, epsilon=epsilon)