Coarse and Fine-grained Forecasting Via Gaussian Process Blurring Effect

This repository contains the code and repoducibility instructions of our Coarse and Fine Grained Forecassting Via Gaussian Process Blurring Effect paper.

Containerized Research Artifact

A containerized snapshot of the research code and reproducibility materials is available through the GitHub Container Registry:

docker pull ghcr.io/sepkfr/fine-grained-gaussian-process-forecasting:latest

Abstract

Time series forecasting is a challenging task due to the existence of complex and dynamic temporal dependencies, leading to inaccurate predictions even by the most advanced models. While increasing training data is a common approach to enhance accuracy, it is often a limitted source. In contrast, we are building on successful denoising approaches for image generation by proposing an end-to-end forecast-blur-denoise framework. By training the parameters of the blur model for best end-to-end performance, we advocate for a clear division of tasks between the forecasting and denoising models. This encourages the forecasting model to learn the coarse-grained behavior, while the denoising model is filling in the blurred fine-grained details.

Requirements

python >= 3.10.4
torch >= 1.13.0
optuna >= 3.0.4
gpytorch >= 1.9.0
numpy >= 1.23.5

Data Loading

python new_data_loader.py --expt_name solar

After running the above python script, a csv file containing the solar dataset is created. In order to generate csv files regarding our other datasets, simply change the expt_name to the desired datatset. You can choose from {traffic, electricity, solar}.

How to run:

Command line arguments:

exp_name: str    the name of the dataset
model_name:str   name of the end-to-end forecasting model (for saving model purpose)
attn_type:str    the type of the attention model (ATA, autofomer, informer, conv_attn)
denoising:bool   whether to use denoising
gp:bool          whether to use our proposed GP noise model 
seed:int         random seed value
cuda:str         which GPU

# one example with traffic dataset and Autoformer forecasting model when apply corruption and denoising with our proposed GP model 

python train.py --exp_name solar --model_name AutoDG--attn_type autofromer --denoising True --gp True --seed 4293 --cuda cuda:0