Currently, I am utilizing ARIMA for forecasting in Python. Below is the code snippet I am working with:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn import datasets, linear_model
from sklearn.model_selection import train_test_split
HSBC = pd.read_csv('HSBC.csv', index_col = 'Date', parse_dates = True)
HSBC2 = HSBC['Close']
result = seasonal_decompose(HSBC2, model='multiplicative', period = 1)
from pmdarima import auto_arima
import warnings
warnings.filterwarnings("ignore")
stepwise_fit = auto_arima(HSBC2, start_p = 1, start_q = 1,
max_p = 3, max_q = 3, m = 12,
start_P = 0, seasonal = True,
d = None, D = 1, trace = True,
error_action ='ignore',
suppress_warnings = True,
stepwise = True)
train = HSBC2[0:173]
test = HSBC2[173:248]
model = SARIMAX(train, order = (0, 1, 1), seasonal_order =(0,1,1,12))
result = model.fit()
start = len(train)
end = len(train) + len(test) - 1
prediction = result.predict(start,end,
typ = 'levels').rename("Predictions")
predictions.plot(legend = True)
test.plot(legend = True)
I am puzzled as to why the x-axis of the prediction plot displays numbers instead of dates like that of the test plot.