Pip install error ModuleNotFoundError: No module named ‘pystan’ in Facebook Prophet

Explanation of the problem

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/private/var/folders/c0/mz0pnjn131d5rsl43kr5my6xh2lw2t/T/pip-build-19fj5puw/fbprophet/setup.py", line 126, in <module>
    """
  File "/Users/miasool/pyenvs/xor_ai/lib/python3.6/site-packages/setuptools/__init__.py", line 143, in setup
    return distutils.core.setup(**attrs)
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/distutils/core.py", line 148, in setup
    dist.run_commands()
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/distutils/dist.py", line 955, in run_commands
    self.run_command(cmd)
  File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/distutils/dist.py", line 974, in run_command
    cmd_obj.run()
  File "/Users/miasool/pyenvs/xor_ai/lib/python3.6/site-packages/wheel/bdist_wheel.py", line 228, in run
    self.run_command('build

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Problem solution for Pip install error ModuleNotFoundError: No module named ‘pystan’ in Facebook Prophet

The installation process of fbprophet requires pystan to be installed first. Pystan is a Python interface to Stan, a probabilistic programming language for Bayesian estimation and inference. It provides a high-level programming interface to perform complex statistical analyses, such as those required by fbprophet.

However, when trying to install fbprophet, one might encounter issues if they try to install a version of pystan that is greater than or equal to 3.0. According to the installation page of FbProphet, pystan >=3.0 is not supported and might cause compatibility problems. The solution to this issue is to install a specific version of pystan, specifically 2.19.1.1, before installing fbprophet.

pip install pystan==2.19.1.1
pip install prophet

By installing the recommended version of pystan before fbprophet, one can avoid compatibility issues and ensure a successful installation process. This can be done by running the two commands shown above, first installing pystan with the specific version and then installing fbprophet. It is important to note that this solution has been tested and confirmed to work by many users, ensuring a smooth installation process.

Other popular problems with Facebook Prophet

$ pip install pystan==2.19.1.1
$ pip install prophet

A brief introduction to Facebook Prophet

Facebook Prophet is a time series forecasting library that was developed by Facebook’s Core Data Science team. It is designed to make the process of creating accurate, reliable, and fast forecasts for time series data easier and more accessible to users, regardless of their level of technical expertise. The library is built on top of the PyStan library, which provides the underlying probabilistic models and statistical computation capabilities.

Prophet uses a decomposable time series model, which involves a trend, seasonality, and holiday component, along with additional user-defined regressors. The library uses a Bayesian optimization approach to fit the parameters of the model to the data and makes predictions based on the historical patterns and trends in the data. Prophet is highly customizable, and users can easily adjust the parameters of the model, such as the number of changepoints, the size of the seasonality component, and the strength of the trend component, to optimize the accuracy of their predictions. Additionally, Prophet provides a built-in capability to handle missing data and outliers, making it a powerful tool for real-world time series forecasting applications.

Most popular use cases for Facebook Prophet

1. Time-series forecasting: Facebook Prophet can be used to forecast time-series data by modeling and identifying patterns in the data. This can be applied to a wide range of time-series data such as stock prices, website traffic, or weather patterns. The following code block demonstrates the implementation of Prophet for time-series forecasting:

import pandas as pd
from fbprophet import Prophet

# Load time-series data into a pandas dataframe
df = pd.read_csv('time-series-data.csv')

# Define the Prophet model and fit it to the data
model = Prophet()
model.fit(df)

# Generate predictions for future dates
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

# Plot the forecast
model.plot(forecast)

2. Anomaly detection: Facebook Prophet can be used to identify anomalies in time-series data. This can be useful for detecting unusual behavior in stock prices, website traffic, or other time-series data. The following code block demonstrates how to use Prophet for anomaly detection:

import pandas as pd
from fbprophet import Prophet

# Load time-series data into a pandas dataframe
df = pd.read_csv('time-series-data.csv')

# Define the Prophet model and fit it to the data
model = Prophet()
model.fit(df)

# Generate a dataframe of anomalies
anomalies = model.diagnostics['anomalies']

# Plot the anomalies
import matplotlib.pyplot as plt
plt.scatter(anomalies['ds'], anomalies['y'])
plt.show()

3. Trends and seasonality analysis: Facebook Prophet can be used to analyze and model trends and seasonality in time-series data. This can be useful for understanding the underlying patterns in stock prices, website traffic, or other time-series data. The following code block demonstrates how to use Prophet for trends and seasonality analysis:

import pandas as pd
from fbprophet import Prophet

# Load time-series data into a pandas dataframe
df = pd.read_csv('time-series-data.csv')

# Define the Prophet model and fit it to the data
model = Prophet()
model.fit(df)

# Generate a plot of the trends and seasonality
model.plot_components(model.plot_components(forecast))