While forecasting using AI allows for smarter and more profitable decision-making, many businesses are still reluctant to take the first leap towards implementing AI solutions. Another case is that they have already started with a proof-of-concept but have no idea what the next steps would be. The main factors are often a lack of resources such as time, expertise, or qualified data.

Built based on the four steps of the data science pipeline, the forecasting tool offers the following benefits at your disposal:

• Data handling. The tool allows data to be imported, connected, or created. Even without any data, a company can start forecasting by generating high-quality synthetic data from business parameters. These parameters can simulate signals from multiple industries, such as sales, energy, IoT, weather, customer behavior, and many more.
• Model development & management. The core of AI is the algorithm at disposal. Therefore, the tool offers many out-of-the-box algorithms that can be trained and validated. With the help of MLFlow, the best forecasting model can be selected, and become reproducible for production environments.
• IT integration. The tool was built to be either a stand-alone product or to be integrated into the IT landscape of any company. Built from open-source technologies, making it affordable.
• Presentation of results. Historical data and forecasts are visually shown for the purpose of model improvement or real business decision-making. Another option is to integrate forecasting results into existing systems, databases, or BI tools.

Future plans include the expansion of the number of techniques that can be used for forecasting. There are also plans to introduce templates that make it easier to generate domain-specific data without much hassle. Furthermore, there are plans of introducing models that can forecast based on hierarchically constrained-sets of forecasting data.