Metode Holt-Winters
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.holtwinters import Holt
from statsmodels.tsa.holtwinters import SimpleExpSmoothing
Data Sampel
Data yang digunakan adalah Natural Gas Consumption (NATURALGAS). Satuan data adalah BCF (miliar kaki kubik).
data = {
"value": {
"2013-01-01": 2878.8,
"2013-02-01": 2567.2,
"2013-03-01": 2521.1,
"2013-04-01": 1967.5,
"2013-05-01": 1752.5,
"2013-06-01": 1742.9,
"2013-07-01": 1926.3,
"2013-08-01": 1927.4,
"2013-09-01": 1767.0,
"2013-10-01": 1866.8,
"2013-11-01": 2316.9,
"2013-12-01": 2920.8,
"2014-01-01": 3204.1,
"2014-02-01": 2741.2,
"2014-03-01": 2557.9,
"2014-04-01": 1961.7,
"2014-05-01": 1810.2,
"2014-06-01": 1745.4,
"2014-07-01": 1881.0,
"2014-08-01": 1933.1,
"2014-09-01": 1809.3,
"2014-10-01": 1912.8,
"2014-11-01": 2357.5,
"2014-12-01": 2679.2,
"2015-01-01": 3115.0,
"2015-02-01": 2925.2,
"2015-03-01": 2591.3,
"2015-04-01": 2007.9,
"2015-05-01": 1858.2,
"2015-06-01": 1899.9,
"2015-07-01": 2067.7,
"2015-08-01": 2052.7,
"2015-09-01": 1901.3,
"2015-10-01": 1987.3,
"2015-11-01": 2249.1,
"2015-12-01": 2588.2,
"2016-01-01": 3091.7,
"2016-02-01": 2652.3,
"2016-03-01": 2356.3,
"2016-04-01": 2083.9,
"2016-05-01": 1965.8,
"2016-06-01": 2000.7,
"2016-07-01": 2186.6,
"2016-08-01": 2208.4,
"2016-09-01": 1947.8,
"2016-10-01": 1925.2,
"2016-11-01": 2159.5,
"2016-12-01": 2866.3,
"2017-01-01": 2913.8,
"2017-02-01": 2340.2,
"2017-03-01": 2523.3,
"2017-04-01": 1932.0,
"2017-05-01": 1892.0,
"2017-06-01": 1910.4,
"2017-07-01": 2141.6,
"2017-08-01": 2093.8,
"2017-09-01": 1920.5,
"2017-10-01": 2031.5,
"2017-11-01": 2357.3,
"2017-12-01": 3086.0,
"2018-01-01": 3340.9,
"2018-02-01": 2710.7,
"2018-03-01": 2796.7,
"2018-04-01": 2350.5,
"2018-05-01": 2055.0,
"2018-06-01": 2063.1,
"2018-07-01": 2350.7,
"2018-08-01": 2313.8,
"2018-09-01": 2156.1,
"2018-10-01": 2285.9,
"2018-11-01": 2715.9,
"2018-12-01": 2999.5,
"2019-01-01": 3424.3,
"2019-02-01": 3019.1,
"2019-03-01": 2927.8,
"2019-04-01": 2212.4,
"2019-05-01": 2134.0,
"2019-06-01": 2119.3,
"2019-07-01": 2393.9,
"2019-08-01": 2433.9,
"2019-09-01": 2206.3,
"2019-10-01": 2306.5,
"2019-11-01": 2783.8,
"2019-12-01": 3170.7,
"2020-01-01": 3320.6,
"2020-02-01": 3058.5,
"2020-03-01": 2722.0,
"2020-04-01": 2256.9,
"2020-05-01": 2072.2,
"2020-06-01": 2127.9,
"2020-07-01": 2464.1,
"2020-08-01": 2399.5,
"2020-09-01": 2151.2,
"2020-10-01": 2315.9,
"2020-11-01": 2442.0,
"2020-12-01": 3182.8,
"2021-01-01": 3343.9,
"2021-02-01": 3099.2,
"2021-03-01": 2649.4,
"2021-04-01": 2265.1,
"2021-05-01": 2117.4,
"2021-06-01": 2238.4,
"2021-07-01": 2412.2,
"2021-08-01": 2433.8,
"2021-09-01": 2142.3,
"2021-10-01": 2262.6,
"2021-11-01": 2693.3,
"2021-12-01": 3007.3,
"2022-01-01": 3612.1,
"2022-02-01": 3064.2,
"2022-03-01": 2785.4,
"2022-04-01": 2379.3,
"2022-05-01": 2247.8,
"2022-06-01": 2326.9,
"2022-07-01": 2597.9,
"2022-08-01": 2566.1,
"2022-09-01": 2263.3,
}
}
data = pd.DataFrame(data)
data.rename(columns={"value": "Natural Gas Consumption(BCF)"}, inplace=True)
data.index = pd.to_datetime(data.index)
data = data.asfreq("MS")
data.head()
| Natural Gas Consumption(BCF) | |
|---|---|
| 2013-01-01 | 2878.8 |
| 2013-02-01 | 2567.2 |
| 2013-03-01 | 2521.1 |
| 2013-04-01 | 1967.5 |
| 2013-05-01 | 1752.5 |
plt.figure(figsize=(12, 6))
sns.lineplot(data=data)
plt.grid()
plt.show()
Pembagian Data
Data bagian akhir (data setelah tahun 2020) akan digunakan untuk memverifikasi kinerja prediksi.
data_train = data[data.index < "2020-1-1"]
data_test = data[data.index >= "2020-1-1"]
plt.figure(figsize=(12, 6))
plt.plot(data_train.index, data_train.values, label="train", linewidth=2)
plt.plot(data_test.index, data_test.values, "-.", label="test")
plt.legend()
plt.grid()
Pelatihan dan Prediksi Model
Dengan merujuk pada dokumentasi statsmodels.tsa.holtwinters.ExponentialSmoothing, parameter akan ditentukan untuk melatih dan memprediksi menggunakan model.
ses = SimpleExpSmoothing(data_train)
ses = ses.fit(smoothing_level=0.1)
ses_pred = ses.forecast(33)
holt = Holt(data_train)
holt = holt.fit(smoothing_level=0.1, smoothing_trend=0.2)
holt_pred = holt.forecast(33)
hw = ExponentialSmoothing(
data_train, trend="additive", seasonal="add", seasonal_periods=12
)
hw = hw.fit()
hw_pred = hw.forecast(33)
plt.figure(figsize=(12, 6))
plt.plot(data_train.index, data_train.values, label="train", linewidth=2)
plt.plot(data_test.index, data_test.values, "-.", label="test")
plt.plot(ses_pred.index, ses_pred.values, "-.", label="prediction(ses)")
plt.plot(holt_pred.index, holt_pred.values, "-.", label="prediction(Holt)")
plt.plot(hw_pred.index, hw_pred.values, "-o", label="prediction(Holt-Winters)")
plt.legend()
plt.grid()
