계절성 고려

SARIMA 모델

• \(SARIMA(p, d, q)(P, D, Q)_m\) 형태로 표현
• \(m\): 계절성 주기
• \(P, D, Q\): 계절성 AR, 차분, MA 차수

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams['font.family'] = 'Noto Sans KR'

df = pd.read_csv('_data/air.csv')

from statsmodels.tsa.seasonal import STL

decomposition = STL(df['Passengers'], period=12).fit()
decomposition.plot()
plt.show()

from statsmodels.tsa.stattools import adfuller

df_diff = np.diff(df['Passengers'], n=1)

ADF_result = adfuller(df_diff)
ADF_result[0], ADF_result[1]

(np.float64(-2.8292668241700047), np.float64(0.05421329028382478))

df_diff = np.diff(df_diff, n=12)

ADF_result = adfuller(df_diff)
ADF_result[0], ADF_result[1]

(np.float64(-17.62486236026156), np.float64(3.823046855601547e-30))

from typing import Union
from statsmodels.tsa.statespace.sarimax import SARIMAX

def optimize_SARIMA(endog: Union[pd.Series, list], order_list: list, d: int, D: int, s: int) -> pd.DataFrame:
    results = []
    for order in order_list:
        try:
            model = SARIMAX(endog, 
                            order=(order[0], d, order[1]), 
                            seasonal_order=(order[2], D, order[3], s),
                            simple_differencing=False).fit(disp=False)
        except:
            continue
        aic = model.aic
        results.append([order, aic])
    result_df = pd.DataFrame(results)
    result_df.columns = ['(p, q, P, Q)', 'AIC']
    result_df = result_df.sort_values(by="AIC").reset_index(drop=True)

    return result_df

from itertools import product

train = df.iloc[:-12]['Passengers']
test = df.iloc[-12:]
#  ps = range(0, 4, 1)
#  qs = range(0, 4, 1)
#  Ps = range(0, 4, 1)
#  Qs = range(0, 4, 1)
# 
#  SARIMA_order_list = list(product(ps, qs, Ps, Qs))
# 
# 
#  d = 1
#  D = 1
#  s = 12
#  SARIMA_result_df = optimize_SARIMA(train, SARIMA_order_list, d, D, s)
#  SARIMA_result_df

SARIMA_model = SARIMAX(train, order=(2, 1, 1), seasonal_order=(1, 1, 2, 12), simple_differencing=False)
SARIMA_model_fit = SARIMA_model.fit(disp=False)

SARIMA_model_fit.plot_diagnostics(figsize=(10, 8))
plt.show()

from statsmodels.stats.diagnostic import acorr_ljungbox

tr = acorr_ljungbox(SARIMA_model_fit.resid, np.arange(1, 11))
tr

	lb_stat	lb_pvalue
1	0.004688	0.945412
2	0.743919	0.689382
3	1.018359	0.796810
4	1.223784	0.874167
5	1.435211	0.920423
6	1.710187	0.944332
7	2.304834	0.941063
8	2.715298	0.950935
9	2.731198	0.974006
10	4.968094	0.893299

SARIMA_pred = SARIMA_model_fit.get_prediction(132, 143).predicted_mean

test['SARIMA_pred'] = SARIMA_pred

sns.lineplot(data=df, x=df.index, y='Passengers', label='실제 값')
sns.lineplot(data=test, x=test.index, y='SARIMA_pred', label='SARIMA 예측값')
plt.xticks(np.arange(0, 145, 12), np.arange(1949, 1962, 1))
plt.xlim(120, 143)