Snowflake customers leverage the Data Cloud to bring all their data together and capitalize on the near-infinite resources of the cloud. But how can this data be used to look ahead? How can we use yesterday’s evidence to plan for tomorrow? The answer—time series forecasting.
Time series forecasting is one of the most applied data science techniques in business. It is used extensively in supply chain management, inventory planning, and finance. Accurate forecasts can establish measurements to guide management, facilitate planning and goal setting, and help mitigate risk. It has practical applications across a wide-range of industries including:
- In healthcare, to forecast public health outcomes such as COVID-19, assess trends and interventions for people with chronic illnesses, and forecast healthcare expenditures.
- In manufacturing, to predict product demand at a geographic level to help meet shipping requirements and reduce inventory waste.
- In energy, to forecast potential spikes in electricity costs based on periods of high load volumes and scarce operating conditions.
Earlier this year, Snowflake announced a focus on building machine learning (ML) extensibility into the Data Cloud. In line with that, Snowflake is announcing its intent to acquire Myst, a company specializing in time series forecasting, and bring the Myst team into Snowflake in order to continue advancing our platform.
Look for more details in the coming months.