AR models (ARIMA, SARIMAX as well) rewrites values, instead of predicting

I have a lot of data from a temperature sensor in a certain installation. I was trying to model it using some auto-regression models. Whenever I do that, the predictions are very similar to current values (see plot later). I am using statsmodels.tsa.arima.model.ARIMA in python. If someone wants to reproduce the code (with sample data):

from statsmodels.tsa.arima.model import ARIMA
import matplotlib.pyplot as plt

train = [86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 85.5, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.6, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.8, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.3, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 88.4, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.1, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.2, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 90.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 89.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 87.1, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.1, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 88.0, 87.6, 87.0, 86.4, 86.0, 85.1, 84.9, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.2, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.8, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.3, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 87.0, 86.5, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 84.1, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.8, 84.0, 84.0, 84.0, 83.7, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.8, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 83.6, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 82.4, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 81.6, 81.0, 81.0, 80.3, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.9, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.5, 82.0, 82.0, 81.6, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 80.8, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.9, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.3, 81.0, 81.0, 81.0, 80.5, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0]
test = [80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 79.9, 79.8, 80.0, 80.0, 80.0, 79.0, 79.4, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 79.6, 79.0, 79.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 78.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.3, 79.6, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.2, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.8, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 81.9, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.5, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.2, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.9, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.9, 85.0, 85.0, 85.0, 85.0, 85.0, 84.9, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.9, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.5, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 85.1, 85.0, 85.0, 84.9, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.4, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.8, 86.0, 86.0, 85.6, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.8, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 86.0, 85.3, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 84.4, 84.0, 84.0, 84.0, 84.2, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.7, 86.0, 86.0, 86.0, 86.0, 85.7, 85.0, 84.0, 83.7, 83.0, 82.4, 82.0, 81.2, 81.0, 80.5, 80.0, 80.0, 81.0, 81.0, 81.0, 81.0, 81.5, 82.0, 82.0, 82.0, 82.5, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.5, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 83.2, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 82.6, 81.8, 81.0, 80.1, 80.0, 79.2, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.6, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.9, 81.0, 81.0, 81.0, 81.0, 81.4, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.4, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.8, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.7, 85.0, 85.0, 85.0, 85.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 83.5, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.8, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.4, 85.0, 84.9, 85.0, 85.0, 85.0, 84.5, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 84.7, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 85.0, 84.9, 84.0, 84.0, 84.0, 84.0, 84.0, 84.0, 83.6, 83.0, 83.0, 83.0, 83.0, 82.2, 82.0, 81.6, 81.0, 80.8, 80.0, 80.0, 80.0, 80.0, 79.2, 79.0, 79.0, 78.7, 78.0, 78.0, 77.4, 77.0, 77.0, 76.6, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.0, 76.3, 77.0, 77.0, 77.0, 77.0, 78.0, 78.0, 78.0, 78.0, 78.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.0, 79.2, 79.5, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 81.0, 81.0, 81.0, 81.0, 80.8, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 80.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.1, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.2, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.9, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 82.4, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 82.7, 82.0, 82.0, 82.0, 81.9, 81.0, 81.0, 81.0, 81.0, 81.0, 81.0, 81.6, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.0, 82.5, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.4, 84.0, 84.0, 84.0, 83.1, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0, 83.0]

model = ARIMA(train, order=(4, 0, 0))
model_fit = model.fit()
forecast = []
for i in range(len(test)):
    forecast.append(model_fit.get_forecast(steps=10).predicted_mean[-1])
    model_fit = model_fit.append([test[i]])

plt.plot(test[10:], label="Test Data", color="seagreen")
plt.plot(forecast[:-10], label="Predicted Data", color="maroon")
plt.legend(loc="upper left")
plt.show()

Plot:
plot of predictions vs actual values
My ideas:

  1. Since this is temperature, it doesn’t change so quickly, so sometimes model predicts on [85, 85, 85, 85], so of course it’s gonna be 85. BUT I tried more regressive values (like AR10, 20, 30) and it didn’t help.
  2. Tried predicting on differences, but the model couldn’t find any pattern (maybe because differences are often very low, and the accuracy of the sensor is 0.1.
  3. I used some more advanced models like ARIMA, or even SARMIAX with cross variables, but it wasn’t really reducing the problem.

Any ideas how to work with this real world data? It would be really nice if I was able to find a appropriate fit using a simple, linear model.
Thanks!

  • time-series
  • augmented-reality
  • forecasting
  • arima

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Filed under: Kiến thức lập trình - @ 19:42

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