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.
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. |
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
You can install fost with pip:
pip install fostool
from fostool.pipeline import PipelineYou 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.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()fost.plot(result)You can fetch sample data on
/examples
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.
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.
We prepared several examples on examples:
- 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).