Python Auto ARIMA模型无法正常工作

我对auto_arima实现没有好运。我已使用statsmodels ARIMA实现将ARIMA适应时间序列。

MSE：715.12

完整代码：

import warnings
import itertools
import pandas as pd
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

#data = sm.datasets.co2.load_pandas()
#y = data.data

y = df
y.drop(y.columns.difference(['delay']), 1, inplace=True)

# The 'MS' string groups the data in buckets by start of the month
y = y['delay'].resample("4H", how="mean")

# The term bfill means that we use the value before filling in missing values
y = y.fillna(y.bfill())

print(y)

y.plot(figsize=(15, 6))
plt.show()

# Define the p, d and q parameters to take any value between 0 and 2
p = d = q = range(0, 2)

# Generate all different combinations of p, q and q triplets
pdq = list(itertools.product(p, d, q))

# Generate all different combinations of seasonal p, q and q triplets
seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]

print('Examples of parameter combinations for Seasonal ARIMA...')
print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[1]))
print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[2]))
print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[3]))
print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[4]))

warnings.filterwarnings("ignore") # specify to ignore warning messages

for param in pdq:
    for param_seasonal in seasonal_pdq:
        try:
            mod = sm.tsa.statespace.SARIMAX(y,
                                            order=param,
                                            seasonal_order=param_seasonal,
                                            enforce_stationarity=False,
                                            enforce_invertibility=False)

            results = mod.fit()

            print('ARIMA{}x{}12 - AIC:{}'.format(param, param_seasonal, results.aic))
        except:
            continue

mod = sm.tsa.statespace.SARIMAX(y,
                                order=(1, 1, 1),
                                seasonal_order=(1, 1, 1, 12),
                                enforce_stationarity=False,
                                enforce_invertibility=False)

results = mod.fit()

print(results.summary().tables[1])

results.plot_diagnostics(figsize=(15, 12))
plt.show()
pred = results.get_prediction(start=pd.to_datetime('2016-11-20 00:00:00'), dynamic=False)
pred_ci = pred.conf_int()

ax = y['2016-10-30 04:00:00':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7)

ax.set_xlabel('Date')
ax.set_ylabel('Delays')
plt.legend()

plt.show()

y_forecasted = pred.predicted_mean
y_truth = y['2016-11-20 00:00:00':]

# Compute the mean square error
mse = ((y_forecasted - y_truth) ** 2).mean()
print('The Mean Squared Error of our forecasts is {}'.format(round(mse, 2)))

pred_dynamic = results.get_prediction(start=pd.to_datetime('2016-11-20 00:00:00'), dynamic=True, full_results=True)
pred_dynamic_ci = pred_dynamic.conf_int()

ax = y['2016-10-30 04:00:00':].plot(label='observed', figsize=(20, 15))
pred_dynamic.predicted_mean.plot(label='Dynamic Forecast', ax=ax)

ax.fill_betweenx(ax.get_ylim(), pd.to_datetime('2016-11-20 00:00:00'), y.index[-1],
                 alpha=.1, zorder=-1)

ax.set_xlabel('Date')
ax.set_ylabel('Delays')

plt.legend()
plt.show()

y_forecasted = pred_dynamic.predicted_mean
y_truth = y['2016-11-20 00:00:00':]

# Compute the mean square error
mse = ((y_forecasted - y_truth) ** 2).mean()
print('The Mean Squared Error of our forecasts is {}'.format(round(mse, 2)))

# Get forecast 500 steps ahead in future
pred_uc = results.get_forecast(steps=32)

# Get confidence intervals of forecasts
pred_ci = pred_uc.conf_int()
print(pred_ci.iloc[:, 1])

ax = y.plot(label='observed', figsize=(20, 15))
pred_uc.predicted_mean.plot(ax=ax, label='Forecast')
ax.fill_between(pred_ci.index,
                pred_ci.iloc[:, 0],
                pred_ci.iloc[:, 1], color='k', alpha=.25)
ax.set_xlabel('Date')
ax.set_ylabel('Delays')

plt.legend()
plt.show()