• Fost
• Framework of Fost
• Quick Start
  • Installation
  • Train with FOST
• Data Format
• Examples
• 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:

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

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 for data preparing.

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.

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

4. Plot results

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
2. Predict States Energy Data
3. Save and load model

Contact Us

• If you have any issues, please create issue here or send messages in gitter.
• For other reasons, you are welcome to contact us by email(fostool@microsoft.com).