In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were s…

A demand forecasting model for an E-Commerce retailer, built using KPIs from Google Analytics & implemented in RStudio. Models: time-series, ARIMA, Regression (multivariate & dynamic). Open-source & contributions welcome.

R package for the Station Demand Forecasting Tool

In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were s…

What's cooking? Which classes get the chop or not

A project for forecasting electrical demand in Victoria, Australia using time series analysis techniques in R.

Optimizes banana supply forecasting using time series models, including Naive, Moving Average, ARIMA, Exponential Smoothing, and TSLM, comparing model performance with MAPE to support accurate demand and export planning.

The goal of this project was to explore demand forecasting using R, analyze trends, seasonality, and create ARIMA models to predict future demand. This README provides an overview of the project and its structure.

Grupo Bimbo inventory demand Kaggle competition

Labelling and Forecasting various pharmaceuticals