📄️ Introduction
This was a project started as an experiment to see whether using covariates like weather and economic indicators provide better forecasts than simply using historical data.
📄️ Evaluation Metric
Choosing a good evaluation metric for our problem is important. How accurate our estimate is depends on what metric we are using, and depending on the situation, different metrics can be the most suitable.
📄️ Data
Delivery data
📄️ Preprocessing
There are several steps followed for preprocessing the data.
📄️ Feature Engineering
After cleaning and preprocessing the data, we created the relevant and important features for forecasting.
📄️ Forecasting
Now that we have the cleaned dataset with the appropriate features, we can finally perform forecasting. This forecasting is just regression, with the exception that train data must be sequential upto a date and test data must be the sequence following that.
📄️ Updating
When the forecast accuracy got from our method is compared to IBP forecasts, we get some brands for which we have significantly higher accuracy/lower errors. We would like to improve the accuracy even further for these brands.
📄️ Comparing
Now that our forecasting is complete, we can compare the forecasts with IBP and get the difference in accuracy and errors.
📄️ Dashboard
The dashboard for presenting the results of this project was made programmatically using the Tremor framework of React Javascript. The web app was made using the NextJS framework and TypeScript.
📄️ Future
This project is a Work in Progress.