Sales Forecasting Model for Retail Chain (Time Series Analysis)
Budget: $30 – $250 USD
We’re seeking a data scientist with strong experience in time series forecasting and multivariate modeling to help build a predictive model for daily sales over the next 6 weeks.
You’ll be working with anonymized historical sales data from 9 high-performing retail stores. The dataset includes store-level and day-level features such as store type, assortment level, promotions, customer count, holidays, and competition metrics.
Key tasks include:
Preprocessing data (handling outliers, standardization)
Checking for stationarity and cointegration
Modeling (e.g., VAR or other approaches depending on data characteristics)
Evaluating performance using MAPE
Delivering forecasts for a defined future period
Knowledge of techniques like Johansen cointegration test, difference modeling, and causal variable analysis is a plus.
Only serious and qualified professionals with demonstrable time series forecasting experience should apply.
You’ll be working with anonymized historical sales data from 9 high-performing retail stores. The dataset includes store-level and day-level features such as store type, assortment level, promotions, customer count, holidays, and competition metrics.
Key tasks include:
Preprocessing data (handling outliers, standardization)
Checking for stationarity and cointegration
Modeling (e.g., VAR or other approaches depending on data characteristics)
Evaluating performance using MAPE
Delivering forecasts for a defined future period
Knowledge of techniques like Johansen cointegration test, difference modeling, and causal variable analysis is a plus.
Only serious and qualified professionals with demonstrable time series forecasting experience should apply.