# How to Save an ARIMA Time Series Forecasting Model in Python

The Autoregressive Integrated Moving Average Model, or ARIMA, is a popular linear model for time series analysis and forecasting.

The statsmodels library provides an implementation of ARIMA for use in Python. ARIMA models can be saved to file for later use in making predictions on new data. There is a bug in the current version of the statsmodels library that prevents saved models from being loaded.

In this tutorial, you will discover how to diagnose and work around this issue.

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• Updated Apr/2019: Updated the link to dataset.

NOTE: The bug discussed in this tutorial appears to
have been fixed in statsmodels version 0.12.1.

How to Save an ARIMA Time Series Forecasting Model in Python
Photo by Les Chatfield, some rights reserved.

## Daily Female Births Dataset

First, let’s look at a standard time series dataset we can use to understand the problem with the statsmodels ARIMA implementation.

This Daily Female Births dataset describes the number of daily female births in California in 1959.

The units are a count and there are 365 observations. The source of the dataset is credited to Newton (1988).

The code snippet below will load and plot the dataset.

Running the example loads the dataset as a Pandas Series, then shows a line plot of the data.

Daily Total Female Births Plot

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## Python Environment

You can do that by running the script below:

Running the script should produce a result showing statsmodels 0.6 or 0.6.1.

You can use either Python 2 or 3.

NOTE: The bug discussed in this tutorial appears to
have been fixed in statsmodels version 0.12.1.

## ARIMA Model Save Bug

We can easily train an ARIMA model on the Daily Female Births dataset.

The code snippet below trains an ARIMA(1,1,1) on the dataset.

The model.fit() function returns an ARIMAResults object on which we can call save() to save the model to file and load() to later load it.

Running this example will train the model and save it to file without problem.

An error will be reported when you try to load the model from file.

Specifically, note the line:

So far so good, so how do we fix it?

## ARIMA Model Save Bug Workaround

Zae Myung Kim discovered this bug in September 2016 and reported the fault.

The bug occurs because a function required by pickle (the library used to serialize Python objects) has not been defined in statsmodels.

A function __getnewargs__ must be defined in the ARIMA model prior to saving that defines the arguments needed to construct the object.

We can work around this issue. The fix involves two things:

1. Defining an implementation of the __getnewargs__ function suitable for the ARIMA object.
2. Adding the new function to ARIMA.

Thankfully, Zae Myung Kim provided an example of the function in his bug report so we can just use that directly:

Python allows us to monkey patch an object, even one from a library like statsmodels.

We can define a new function on an existing object using assignment.

We can do this for the __getnewargs__ function on the ARIMA object as follows:

The complete example of training, saving, and loading an ARIMA model in Python with the monkey patch is listed below:

Running the example now successfully loads the model without error.

## Summary

In this post, you discovered how to work around a bug in the statsmodels ARIMA implementation that prevented you from saving and loading an ARIMA model to and from file.

You discovered how to write a monkey patch to work around the bug and how to demonstrate that it has indeed been fixed.

Did you use this workaround on your project?

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### 30 Responses to How to Save an ARIMA Time Series Forecasting Model in Python

1. n1k31t4 June 3, 2017 at 11:09 pm #

Hi Jason, thanks for the workaround.
I am using statsmodels 0.8.0 and have been trying to save a dictionary of fitted models using pickle itself i.e. not using the built-in method. I am still getting the error you mention above about the ARIMA object not having a dates attribute. Why it is trying to look for a dates attribute anyway?
Can you think of a work around for this case?

2. Veeral June 10, 2017 at 6:56 am #

Hi Dr. Brownlee,
Thank you for sharing. I tried to adjust this workaround so that it can work for seasonal ARIMA modeling but I am still having issues. I would like to save the model into a pickle file but I keep receiving the following error: “Type error: can’t pickle statsmodels.tsa.statespace._statespace.dStatespace objects”

Do you have an idea of how I can go about working around this issue? Here is some of the code I am working with.

mod = sm.tsa.statespace.SARIMAX(
timeseries,
order=(1, 0, 1),
seasonal_order=(0, 1, 1, 28),
enforce_stationarity=False,
enforce_invertibility=False
)

self.trained_model = mod.fit()
self.trained_model.save(‘call model.p’)

Thanks!

• Jason Brownlee June 10, 2017 at 8:31 am #

Sorry, I have not tried.

Perhaps reach out to the developer that discovered the workaround, he may have some ideas about SARIMAX.

• Ankit July 1, 2017 at 6:24 am #

Hi Veeral,

Were you able to find the fix for your issue? I am also facing the same issue with SARIMA model.

• Jay B September 21, 2017 at 12:36 am #

Hey everyone,
I am also having the same problem with SARIMAX models; has anyone found a solution?

3. Ankit Tripathi June 2, 2018 at 8:53 pm #

Hey Jason,
Thanks for the article.

I am using ARIMA to fit values and save it as a pickle file. Post that, the pickle file is used to get out of sample predictions. However, while getting sample predictions I am getting the following error: Cannot cast ufunc subtract output from dtype(‘float64’) to dtype(‘int64’) with casting rule ‘same_kind’. Do you have any idea about the cause?

• Jason Brownlee June 3, 2018 at 6:22 am #

I have not seen this sorry.

Perhaps try posting to stackoverflow?

4. Ankit Tripathi June 13, 2018 at 5:22 pm #

Hey Jason, thanks for the awesome post! I followed this and saved a pickle file with a certain ARIMA order. The model was successfully fit and then saved as pickle file. However, when I load the pickle file to get out of sample forecasts, I get null values for any number of steps. Do you have any idea about this issue?

• Ankit Tripathi June 13, 2018 at 9:38 pm #

So basically, after fitting the model, model_fit.summary() throws error. I know that its not a code error solving platform, but I think it is a very general case if model summary is throwing error after fitting the model!

• Jason Brownlee June 14, 2018 at 6:02 am #

That is odd. Perhaps comment out that line?

• Jason Brownlee June 14, 2018 at 5:58 am #

Sorry, I have not seen this issue. Perhaps ensure that you are using the most recent version of Statsmodels?

5. Ankit Tripathi June 14, 2018 at 6:36 pm #

Jason, the problem is that , after reading the pickle file, out of sample forecasts give out null values. I think , this is probably a bug, Will try to report it on Git. Thanks.

• Jason Brownlee June 15, 2018 at 6:44 am #

That is a shape.

I have some examples in the blog of using the coefficients directly, without the wrapper class. Perhaps that would be a good workaround?

• sanjie October 9, 2018 at 9:17 pm #

hello Jason,
thanks for your article, i use statsmodels 0.9.0, there is not this kind of bug.

• Archana Dwivedi August 30, 2020 at 2:31 am #

Hello Jason,
Do you have idea of azure ml pipeline.There is a module there “create python model” module. But I am not getting how will it work and how will the parameters be passed. Thanks in advance

• Jason Brownlee August 30, 2020 at 6:45 am #

Sorry, I have never used the MS platform.

6. karim May 22, 2019 at 3:52 am #

Hello, Thanks for your nice post. But I am confused about how to use the loaded model to predict the feature. For example, I am giving here the code(which is also may be taken from one of your tutorials as I am learning all of these stuff with your resources):

Actual = [x for x in train_set]
Predictions = list()

#Function that calls ARIMA model to fit and forecast the data
def StartARIMAForecasting(Actual, P, D, Q):
model = ARIMA(Actual, order=(P, D, Q))
model_fit = model.fit(disp=0)
prediction = model_fit.forecast()[0]
return prediction

for timepoint in range(len(test_set)):
ActualValue = test_set[timepoint]
#forcast value
Prediction = StartARIMAForecasting(Actual, 2,1,2)
print(‘Actual=%f, Predicted=%f’ % (ActualValue, Prediction))
Predictions.append(Prediction)
Actual.append(ActualValue)

Now, here, Where I can write the line to save the model and after saving and loading what will be the correct line to do the prediction? I am eagerly waiting to get the solution. 🙂

N:B: In my dataset, I have used datetime as index.

7. MK February 13, 2021 at 11:58 pm #

Hi, I have been trying your saved LSTM model code with my deployment for time series forecasting quite a long time. Now i m facing a problem which stuck me for a while, can you please share me some tips

below is my code:

model.py

from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.arima_model import ARIMAResults
import warnings
warnings.filterwarnings(“ignore”)

# monkey patch around bug in ARIMA class
def __getnewargs__(self):
return ((self.endog),(self.k_lags, self.k_diff, self.k_ma))
ARIMA.__getnewargs__ = __getnewargs__

# prepare data
X = series.values
X = X.astype(‘float32’)
# fit model
model = ARIMA(X, order=(1,1,1))
model_fit = model.fit()
# save model
model_fit.save(‘model.pkl’)

app.py

import io
from io import StringIO
import csv
import pandas as pd
import numpy as np
import pickle
import os
from statsmodels.tsa.arima_model import ARIMAResults

def transform(text_file_contents):
return text_file_contents.replace(“=”, “,”)

@app.route(‘/’)
def form():
return “””

Let’s TRY to Predict..

Predict

“””
@app.route(‘/transform’, methods=[“POST”])
def transform_view():
if request.method == ‘POST’:
f = request.files[‘data_file’]
if not f:
return “No file”

#print(“file contents: “, file_contents)
#print(type(file_contents))
print(csv_input)
for row in csv_input:
print(row)

stream.seek(0)

# load the model from disk
dataset = df.values
dataset = dataset.astype(‘float32’)
dataset = np.reshape(dataset, (-1, 1))
df = model.predict(dataset)

response = make_response(df.to_csv())
return response

if __name__ == “__main__”:
app.run(debug=True, port = 9000, host = “localhost”)

• Jason Brownlee February 14, 2021 at 5:09 am #

Sorry, I don’t have the capacity to review/debug your code, perhaps you can summarize the specific issue you are having?

• MK February 14, 2021 at 3:22 pm #

KeyError: ‘The start argument could not be matched to a location related to the index of the data.’

• Jason Brownlee February 15, 2021 at 5:43 am #

The error suggests that whatever you are specifying a range of data, such as to the predict function, that the index for the starting point of the range is not valid for your dataset.

Perhaps you can print the length of your dataset and the start index your trying to use and compare them directly.

8. JG January 20, 2022 at 7:15 pm #

Error encountered. Can you help on this.

—————————————————————————
ModuleNotFoundError Traceback (most recent call last)
in