Train22=df22[1:424] Test22=df22[424:len(df22)] model=pm.auto_arima(Train22['Price'],m=12,seasonal=True,start_q=0,star_p=0,max_order=5,error_action='ignore',stepwise=True,trace=True) # In[63]: model.summary() # In[64]: model.plot_diagnostics() # In[76]: prediction=pd.DataFrame(model.predict(n_periods=423),index=Test22.index) prediction.columns=['predicted_sales'] # In[77]: prediction
model = pm.auto_arima(df.value, start_p=1, start_q=1,
test='adf', # use adftest to find optimal 'd'
max_p=3, max_q=3, # maximum p and q
m=1, # frequency of series
d=None, # let model determine 'd'
seasonal=False, # No Seasonality
start_P=0,
D=0,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
print(model.summary())
model.plot_diagnostics(figsize=(7,5))
plt.show()
# Top left: The residual errors seem to fluctuate around a mean of zero and have a uniform variance.
#
# Top Right: The density plot suggest normal distribution with mean zero.
#
# Bottom left: All the dots should fall perfectly in line with the red line.
# Any significant deviations would imply the distribution is skewed.
#
# Bottom Right: The Correlogram, aka, ACF plot shows the residual errors are not autocorrelated.
# Any autocorrelation would imply that there is some pattern in the residual errors which are not explained in the model.
# So you will need to look for more X’s (predictors) to the model.
# Forecast
n_periods = 24
