What is time series forecasting? Time series data typically refers to historical data collected at regular time intervals over a period of time. For example, sales data of a store recorded on a daily basis or expense data recorded on a monthly basis are examples of times series data. Predictive forecasting is using machine learning or statistics to forecast future values based on these historical time series data analyzing trends, cycles, and fluctuations in the data.Predictive forecasting often goes hand in hand with planning forecasting, where controllers are concerned with a company’s financial direction, outcome, and overall plan execution on a monthly or quarterly basis.
In addition to historical data and predictive forecasting, a planning forecast often utilizes business acumen and planning models to determine these future outcomes. Thus, the future values predicted through a predictive forecast can be leveraged in a planning forecast to make more data-driven decisions.
Real-World Examples of Time Series Forecasting
Time series forecasting is a useful predictive capability that is enabling businesses to forecast and improve planning for the future. Being able to predict and better prepare for the future can help a business optimize its resources and be prepared for what is to come. Here are two scenarios where our customers were able to benefit from predictive forecasting.
- Sales forecasting: A retail business utilized predictive forecasting in analyzing historic sales data to produce sales forecasts for their stores. A sales forecast can reveal seasonal fluctuation patterns in order to better gauge and prepare for spikes of consumer demand for certain products at specific times, such as during sports events. This helps the business better plan its inventory to anticipate the different levels of consumer demand based on the sales forecast.
- Predicting travel expenses: A global organization leveraged predictive forecasting for predicting future travel expenses, which led to better budget planning. This organization needed to plan how they should be budgeting for their employees’ travel expenses for the next quarter. They were able to use historic travel expense data to predict upcoming travel expense values. Making use of this data-driven forecast, the organization is able to optimize its financial resources through better budgeting and can monitor travel expenses that deviate from the forecast significantly for further investigation.
How to Use Time Series Forecasting in SAP BusinessObjects Cloud
Time series forecasting embedded in SAP BusinessObjects Cloud provides a business user-friendly interface where users can simply select their desired forecasting periods and click “run” without needing to choose between different time series algorithms.
You can get more details about predictive forecasting in this post on the SAP community blog.
And for more on predictive analytics topics, check out our Predictive Thursdays blog series.
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