Time-Series Forecasting for CFOs

As a CFO, understanding time-series forecasting, specifically through methods like ARIMA (AutoRegressive Integrated Moving Average) and Exponential Smoothing, is crucial for effective financial planning and analysis.

These forecasting techniques help predict future financial outcomes based on historical data, enabling you to make more informed decisions regarding budgeting, strategic planning, and resource allocation.

Here’s a breakdown of what you need to know about the most popular methods if you are a CFO (or if you are in the way to be one):

ARIMA

What is it?

ARIMA is a popular statistical method used to analyze and forecast time-series data, especially when the data is non-stationary (i.e., its statistical properties such as mean and variance change over time).

What are it’s key components?

• AR (AutoRegression): This component models the changing variable as a linear combination of its past values, indicating that past values have a linear influence on current values.
• I (Integrated): Involves differencing the data (i.e., subtracting the previous observation from the current observation) one or more times to make the time series stationary.