Cannot plot predicted time series values using matplotlib

Question

I am trying to plot my actual time series values and predicted values but it gives me this error:

ValueError: view limit minimum -36816.95989583333 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units

I am using statsmodels to fit an arima model to the data.

This is a sample of my data:

datetime             value
2017-01-01 00:00:00  10.18
2017-01-01 00:15:00  10.2
2017-01-01 00:30:00  10.32
2017-01-01 00:45:00  10.16
2017-01-01 01:00:00  9.93
2017-01-01 01:15:00  9.77
2017-01-01 01:30:00  9.47
2017-01-01 01:45:00  9.08

This is my code:

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

results = mod.fit()
pred_uc = results.get_forecast(steps=500)
pred_ci = pred_uc.conf_int(alpha = 0.05)

# Plot
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(1, 1, 1)
ax.plot(subset,color = "blue")
ax.plot(pred_uc.predicted_mean, color="black", alpha=0.5, label='SARIMAX')
plt.show()

Any idea how to fix this?

Answer 1

It should have been an issue on how you provide the data.

The datetime values must be the index of the values in your data subset variable, and thus, the following works.

I've imported the data as follows right before the code you provided:

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import statsmodels.api as sm

subset = pd.Series(

[

    10.18, 10.2 , 10.32,
    10.16, 9.93, 9.77,
    9.47, 9.08

]

, index=pd.date_range(

    start='2017-01-01T00:00:00.000',

    end='2017-01-01T01:45:00.000',

    freq='15T'

)  )

And I got, I believe, your desired plot (it's cut):

.

I used these versions of the libraries, in Python 3:

matplotlib.version '3.1.2'

numpy.version '1.17.4'

pandas.version '0.25.3'

statsmodels.version '0.12.0'