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FOST

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FOST is a general forecasting tool, which demonstrate our experience and advanced technology in practical forecasting domains, including temporal, spatial-temporal and hierarchical forecasting. Current general forecasting tools (Gluon-TS by amazon, Prophet by facebook etc.) can not process and model structural graph data, especially in spatial domains, also those tools suffer from tradeoff between usability and accuracy. To address these challenges, we design and develop FOST and aims to empower engineers and data scientists to build high-accuracy and easy-usability forecasting tools.

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Download Latest Version (.zip)

- 1. Cosine prediction.ipynb
- 2. Predict States Energy Data.ipynb
- 3. Save and load.ipynb
- - __init__.py
  - default.yaml
- - __init__.py
  - data_utils.py
- - __init__.py
  - gcn.py
  - gconv.py
  - input_fixer.py
  - krnn.py
  - mlp.py
  - sandwich.py
- - __init__.py
  - config_handler.py
  - fusion.py
  - logger.py
  - loss.py
- - __init__.py
  - predictor.py
  - trainer.py
  - utils.py
- - __init__.py
  - plot.py
- __init__.py
- pipeline.py
.gitignore
CODE_OF_CONDUCT.md
framework.jpg
LICENSE
MANIFEST.in
README.md
SECURITY.md
setup.py
SUPPORT.md

# Installation Guide

1. Get the code

git clone https://github.com/microsoft/FOST

Downloads the entire project code from GitHub to your computer.

cd FOST

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install fostool

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install fostool

Installs the package published on PyPI directly — no need to clone the source.

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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<p align="center">
  <img src="https://dsm01pap002files.storage.live.com/y4mueD2H6WE6Df3edTW6YE_KLeND5ROVCKksKxGarweSuk9VW2m4jrY8EBTVN5qXiQEnuyfSQZ2t9HOtrsLjXSPqKkmMrMtrmncb3xVzITPl0pu7mwESEjF1CooSkvtdTNPBW237K1bTNqyA9cD-opu_ISObWFLusFpAFJQk_tSxRAYi-mp4QI9fcXUUTYgndae?width=4248&height=1324&cropmode=none" width=50% />
</p>

- [**Fost**](#fost)
- [Framework of Fost](#framework-of-fost)
- [Quick Start](#quick-start)
  - [Installation](#installation)
  - [Train with FOST](#train-with-fost)
- [Data Format](#data-format)
- [Examples](#examples)
- [Contact Us](#contact-us)

# FOST

FOST(Forecasting open source tool) aims to provide an easy-use tool for spatial-temporal forecasting. The users only need to organize their data into a certain format and then get the prediction results with one command. FOST automatically handles the missing and abnormal values, and captures both spatial and temporal correlations efficiently.

# Framework of FOST

Following is the framework of FOST, basically it contains 4 main components:

![FOST framework](https://dsm01pap002files.storage.live.com/y4mqv6c15r0vEfpNGcpMnUa4sOxYZFDDBL6h47EdLlVuKZcGTUw8LKrseJnZ2Q8hlJK3VB0lj13TJmF5pvrC5LeiKHR4cfSIGJT3YmV2D_-O6HpG8VFVKM5Alx9hEhAvc0fOAXFkthsC5qAccx8_eJsoKj8eTHvAns0z72v811JOVbswqGLWOeGNyUIjgQiL52F?width=1050&height=268&cropmode=none)

| Module name   | Description                                                  |
| ------------- | ------------------------------------------------------------ |
| Preprocessing | Preprocessing module aims at handle varies data situation, currently FOST designed sub-module to handle issues such as missing value, unalignment timestamp and feature selection. |
| Modeling      | FOST contains implements for different mainstream deep learning models such as RNN, MLP and GNN, for better performance on varies custom data. Further model implements such as Transformer, N-beats are in progress. |
| Fusion        | Fusion module aims at automatically select and ensemble model predictions. |
| Utils         | There are many other utils in FOST, such as neural-network trainer and predictor, result plotter and so on. |

# Quick Start

## Installation

### Installation of dependency packages

#### 1. Prerequisites

This project relies on `pytorch >= 1.8` and `torch-geometric >= 1.7.2`

- torch installation reference link：https://pytorch.org/get-started/previous-versions/

- torch-geometric installation reference link: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html

#### 2. Installation

You can install fost with pip:

```
pip install fostool
```

## Train with FOST

#### 1. Import forecasting pipeline

```python
from fostool.pipeline import Pipeline
```

#### 2. Setting data path and lookahead

You need to pass your `train.csv` and `graph.csv` for model training, see [dataformat](#data-format) for data preparing.

```python
train_path = '/path/to/your/train.csv'
graph_path = '/path/to/your/graph.csv' # graph_path is alternative
lookahead = 7 # Forward steps you would like to predict.
```

#### 3. Fit and predict

We provide a default config file in config/default.yaml. You could use your config file through config_path augment.

```python
fost = Pipeline(lookahead=lookahead, train_path=train_path, graph_path=graph_path)
fost.fit()
result = fost.predict()
```

#### 4. Plot results

```python
fost.plot(result)
```

# Data Format

> You can fetch sample data on `/examples`

### 1. train.csv

3 columns are required for `train.csv`:

+ Node: node name for current data
+ Date: date or timestamp for current data
+ TARGET: target for prediction

A valid format may look like:

| Node    | Date       | TARGET     |
| ------- | ---------- | ---------- |
| Alaska  | 1960-01-01 | 800592.0   |
| Alaska  | 1961-01-01 | 933600.0   |
| Alabama | 1960-01-01 | 10141633.0 |
| Alabama | 1961-01-01 | 9885992.0  |
| Alabama | 1962-01-01 | 10497917.0 |

Columns except above will be regarded as feature columns.

### 2. graph.csv (option)

`graph.csv` should only contains 3 columns:

+ node_0: node name for fist node, node name should align with node name in `train.csv`.
+ node_1: node name for second node, node name should align with node name in `train.csv`.
+ weight: weight on connection for node_0 to node_1.

If `graph.csv` is not provided, identity graph will be used.

# Examples
We prepared several examples on `examples`:

1. [Predict simulation cosine curve](/examples/1.%20Cosine%20prediction.ipynb)
2. [Predict States Energy Data](/examples/2.%20Predict%20States%20Energy%20Data.ipynb)
3. [Save and load model](/examples/3.%20Save%20and%20load.ipynb)

# Contact Us

- If you have any issues, please create issue [here](https://github.com/microsoft/fost/issues/new/choose) or send messages in [gitter](https://gitter.im/Microsoft/fost).
- For other reasons, you are welcome to contact us by email([fostool@microsoft.com](mailto:fostool@microsoft.com)).