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```bash
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conda install -c saravji pmdarima
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```
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- **Step 4**: Clone the latest Forecasting Toolbox version that can be found in the present Gitlab repository of the Forecasting Toolbox and navigate to the home directory.
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- **Step 4**: Clone the latest Forecasting Toolbox version that can be found in the present Gitlab repository of the Forecasting Toolbox and navigate to the root directory.
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- **Step 5**: To start the server, use the command promt inside the active environment and execute the commands described in section [Run Server](Forecaster Toolbox Run Server).
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## Installation using Docker Build
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In this section, we provide instructions on how the user can build from scratch a new Docker Image that contains the python Flask app and the Conda environment of the of the Forecasting Toolbox.
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- **Step 1**: Download and install [Docker](https://www.docker.com/)
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- **Step 2**: Clone the latest Forecasting Toolbox version that can be found in the present Gitlab repository of the Forecasting Toolbox and navigate to the home directory. You should see a *DockerFile* and a *environment.yml* file, which contains the Conda environment dependencies.
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- **Step 2**: Clone the latest Forecasting Toolbox version that can be found in the present Gitlab repository of the Forecasting Toolbox and navigate to the root directory. You should see a *DockerFile* and a *environment.yml* file, which contains the Conda environment dependencies.
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- **Step 3**: In the home directory of the Forecasting Toolbox, open cmd and execute the following command:
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```bash
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sudo docker build -t forecaster_toolbox .
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