使用 statsmodels.tsa.arima.model import ARIMA 进行时间序列预测

Question

I am trying to make prediction on my ARIMA Model but I stucked in one point

from statsmodels.tsa.arima.model import ARIMA
train2 = trainData1["meantemp"][:1170]
test2 = trainData1["meantemp"][1170:]
# p,d,q ARIMA Model
model = ARIMA(train2, order=(1,1,50))
model_fit = model.fit()
print(model_fit.summary())

Here, trainData1 has date as index (both train2 and test2 as you guess) and trained model with train2 data, after that I tried to make prediction on test2 data as follows;

# make predictions
predictions = model_fit.predict(test2)
rmse = mean_squared_error(test2.values, predictions)
rmse

But it gives me the following error;

TypeError: Cannot convert input [date
2016-03-17    2.375000
2016-03-18   -0.125000
2016-03-19    0.598214
2016-03-20    0.347619
2016-03-21   -0.508333
                ...   
2016-12-28    0.367391
2016-12-29   -1.979296
2016-12-30   -1.142857
2016-12-31    0.957393
2017-01-01   -5.052632
Name: meantemp, Length: 291, dtype: float64] of type <class 'pandas.core.series.Series'> to Timestamp

What should be added to inside of predict function as data?

train2 as follows;

2013-01-02   -2.600000
2013-01-03   -0.233333
2013-01-04    1.500000
2013-01-05   -2.666667
2013-01-06    1.000000
                ...   
2016-03-12   -0.504167
2016-03-13   -0.312500
2016-03-14   -1.875000
2016-03-15    1.691667
2016-03-16   -0.129167
Name: meantemp, Length: 1170, dtype: float64

test2 as follows;

date
2016-03-17    2.375000
2016-03-18   -0.125000
2016-03-19    0.598214
2016-03-20    0.347619
2016-03-21   -0.508333
                ...   
2016-12-28    0.367391
2016-12-29   -1.979296
2016-12-30   -1.142857
2016-12-31    0.957393
2017-01-01   -5.052632
Name: meantemp, Length: 291, dtype: float64

Answer 1

I solved my own problem as follows;

# make predictions
predictions = model_fit.forecast(291)
print(f'ARIMA Model Test Data MSE: {np.mean((predictions.values - test2.values)**2):.3f}')

with forecast function, model predicted next 291 step which is day here as it is daily time-series and made evaluation with MSE metric using predictions and actual test values.