step-by-step guideline to reproduce the results

install dependencies

put all the github repo in a single folder and install the packages listed in lines 7-25

choose the experiment to run

6 hyperparameters to fix in lines 32-70 in main.py For reproducibility of the results, choose the translation term as mentioned below in section "reproducibility"

Training

run main.py The expected output: 1- training & validation metrics per epoch 2- training & testing metrics at the end of the training 3- plot of the actual data vs the predictions

plot information

boxplot.py : used to plot the boxplots (figures 10, 11, 12) correlation_analysis.py: used to choose the independant variables of the model data_cleaning.py: clean the data using IQR method data_visualization.py: visualize the load data dataloader.py: used to prepare the data for training feature_engineering.py: contains the detailed feature engineering process models.py: contains the architecture of our DAF model and the benchmarks train_validation.py: describes the training process (with DA and without DA) pvalue.py: computes the pvalues (table 9)

reproducibility

choose random_seed=700

if target_data=="Billi": *weeks=20: translation=2 *weeks=10: translation=2 *weeks=5: translation=2

if target_data=="Maryann": *weeks=20: translation=4 *weeks=10: translation=4 *weeks=5: translation=8

if target_data=="Antonina": *weeks=20: translation=1 *weeks=10: translation=5 *weeks=5: translation=2