Time series forecasting is a vital tool for organizations looking to make informed planning decisions. Amazon has a long history of using this approach, and has now integrated its advanced forecasting offerings with modern machine learning (ML) algorithms in its no-code workspace, Amazon SageMaker Canvas. The platform allows data preparation using natural language, the building and training of highly accurate models, the generation of predictions, and the deployment of models to production, all without writing a line of code.
A significant factor that can influence forecasting across many domains is weather. Understanding and incorporating weather forecasts allow for more accurate planning and decision-making. For instance, energy companies use weather forecasts to predict energy demand, agribusinesses use weather data to forecast crop yields, while airlines use them to schedule staff and equipment efficiently.
Firstly, organizations need to find a weather data provider. Factors to consider include price, time coverage, time resolution, data capture method, geography, and specific weather features provided by each service.
Once a provider is chosen, a weather data ingestion process is developed. SageMaker Canvas can help in this process. The data harvested from the provider is combined with the organization’s own historic data for forecasting. With future-dated data also included, it improves the forecasting capability.
Next, this data is combined with business data for time series model building. With this, organizations can build models with and without weather data to determine how much of an impact weather data has on forecasts.
Cleaning up to stop consumption of SageMaker Canvas workspace instance hours is essential, to release all resources used by the workspace instance.
In conclusion, using weather data in time series forecasting can lead to more accurate outcomes. Amazon’s SageMaker Canvas aids in this process by offering a no-code workspace that combines data from various sources, generates models, and deploys them to production. This tool could be revolutionary for organizations aiming to improve their forecasting outcomes.
