You want to know if your sales figures are on track to hit target this month, or if you need to order additional stock due to an upturn in demand.
How can you build a forecast model predicts accurately into future and is robust to seasonality and trend?
Our forecasting models are trained on your historical data and dynamically update to account for changes in trend and seasonality.
The models can either be built directly on raw time series data, or use machine learning to incorporate additional features and parallel time series that may influence the forecast.
We set up metrics and dashboards to allow you to monitor the accuracy and volatility of the forecast over time and understand the feature that have the greatest influence the prediction.
Time series to be forecasted
Additional static or time series data to be included in the model
1. A forecast model that trains automatically and predicts a given number of time steps ahead
2. Deployment of the model onto your own on-premises server or cloud hosted architecture.
3. Dashboards set up to allow stakeholders to monitor the forecast over time and assess model performance and feature importance.
The model will take the guesswork out of target setting and demand forecasting, allowing you to objectively understand the performance of your business and plan ahead accordingly.