# Machine-Learning-for-Time-Series-Forecasting

**Repository Path**: TerryPro/Machine-Learning-for-Time-Series-Forecasting

## Basic Information

- **Project Name**: Machine-Learning-for-Time-Series-Forecasting
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-08-23
- **Last Updated**: 2025-08-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Machine Learning for Time Series Forecasting with Python

To configure your environment you will need Anaconda, the Python Distribution.

The instructions for installing Anaconda can be found [here](https://docs.anaconda.com/anaconda/install/)

Once Anaconda is installed you should have `conda` executable in your environment path.

Anaconda provides a concept called environments which allow us to have different dependencies based on what we're working on. The documentation is available [here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)

We would like to use different conda environment for running jobs on our machines, but when we submit jobs to Azure Machine Learning we will use a different environment. To provide easy access to these environments in jupter notebooks or jupyter lab we can use `nb_conda` into Anaconda's base environment.

```{bash}
conda install -n base nb_conda
```