jokeroor - 1 year ago 101

Python Question

I am trying to predict outofsample values for an array.

Python code:

`import pandas as pd`

import numpy as np

from statsmodels.tsa.arima_model import ARIMA

dates = pd.date_range('2012-07-09','2012-07-30')

series = [43.,32.,63.,98.,65.,78.,23.,35.,78.,56.,45.,45.,56.,6.,63.,45.,64.,34.,76.,34.,14.,54.]

res = pd.Series(series, index=dates)

r = ARIMA(res,(1,2,0))

pred = r.predict(start='2012-07-31', end='2012-08-31')

I am getting this error.I see I have given two argument but compiler return I have given 3.

`Traceback (most recent call last):`

File "XXXXXXXXX/testfile.py", line 12, in <module>

pred = r.predict(start='2012-07-31', end='2012-08-31')

TypeError: predict() takes at least 2 arguments (3 given)

Please help

Answer Source

The call signature of `ARIMA.predict`

is

```
predict(self, params, start=None, end=None, exog=None, dynamic=False)
```

Thus, when you call `r.predict(start='2012-07-31', end='2012-08-31')`

, `self`

gets bound to `r`

, and values are bound to `start`

and `end`

but the required positional arument `params`

does not get bound. That is why you get the error

```
TypeError: predict() takes at least 2 arguments (3 given)
```

The "3 given" refer to `r`

, `start`

and `end`

. The "2 arguments" refer to the two required arguments, `self`

and `params`

.

Unfortunately the error message is misleading. Obviously, 3 is greater than 2. The problem is that the *required* positional argument `params`

was not given.

To fix the problem, you need parameters. Usually you find those parameters by fitting:

```
r = r.fit()
```

before calling

```
pred = r.predict(start='2012-07-31', end='2012-08-31')
```

`r.fit()`

returns a `statsmodels.tsa.arima_model.ARIMAResultsWrapper`

which
have the parameters "baked in" so calling `ARIMAResultWrapper.fit`

does not require passing `params`

.

```
import pandas as pd
import numpy as np
from statsmodels.tsa.arima_model import ARIMA
dates = pd.date_range('2012-07-09','2012-07-30')
series = [43.,32.,63.,98.,65.,78.,23.,35.,78.,56.,45.,45.,56.,6.,63.,45.,64.,34.,76.,34.,14.,54.]
res = pd.Series(series, index=dates)
r = ARIMA(res,(1,2,0))
r = r.fit()
pred = r.predict(start='2012-07-31', end='2012-08-31')
print(pred)
```

yields

```
2012-07-31 -39.067222
2012-08-01 26.902571
2012-08-02 -17.027333
...
2012-08-29 0.532946
2012-08-30 0.532447
2012-08-31 0.532780
Freq: D, dtype: float64
```