10 top forecasting techniques for FP&A
Quote from bsdinsight on 2 May 2025, 08:3910 top forecasting techniques for FP&A.
𝗧𝗥𝗘𝗡𝗗 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦
Involves examining historical data to identify patterns or trends over time. Simple, cost-effective, but may not account for unexpected changes.𝗧𝗜𝗠𝗘 𝗦𝗘𝗥𝗜𝗘𝗦 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦
Analyzing a sequence of data at specific time intervals. The primary goal is to identify one-offs, seasonal variations, and cycles to forecast future values.𝗦𝗘𝗔𝗦𝗢𝗡𝗔𝗟 𝗗𝗘𝗖𝗢𝗠𝗣𝗢𝗦𝗜𝗧𝗜𝗢𝗡
Breaks down time series data into seasonal, trend, and residual components. Suitable for data with strong seasonal patterns.𝗘𝗫𝗣𝗘𝗥𝗧 𝗝𝗨𝗗𝗚𝗠𝗘𝗡𝗧
Using insights, intuition, and experience of individuals or groups with deep knowledge in a specific area to forecast future events or trends.𝗘𝗫𝗣𝗢𝗡𝗘𝗡𝗧𝗜𝗔𝗟 𝗦𝗠𝗢𝗢𝗧𝗛𝗜𝗡𝗚
A weighted moving average method where more recent data points have a higher influence. Effective for short-term.𝗥𝗘𝗚𝗥𝗘𝗦𝗦𝗜𝗢𝗡
It models the relationship between a dependent variable and one or more independent variables. Used when a relationship between variables is suspected.𝗠𝗢𝗡𝗧𝗘 𝗖𝗔𝗥𝗟𝗢 𝗦𝗜𝗠𝗨𝗟𝗔𝗧𝗜𝗢𝗡
A probabilistic forecasting technique that uses random sampling to simulate a range of possible outcomes. Ideal for assessing risk and uncertainty.𝗠𝗢𝗩𝗜𝗡𝗚 𝗔𝗩𝗘𝗥𝗔𝗚𝗘𝗦
This method smooths out fluctuations in data by averaging past data points over a specific period. Ideal for short-term forecasting.𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚
Uses algorithms that learn from data to predict future outcomes. Can identify non-linear relationships and complex patterns.𝗛𝗜𝗦𝗧𝗢𝗥𝗜𝗖𝗔𝗟 𝗚𝗥𝗢𝗪𝗧𝗛 𝗥𝗔𝗧𝗘𝗦
Involve using the growth rate of a particular variable (e.g., revenue, sales, market size) from past periods as a basis for forecasting future growth.
10 top forecasting techniques for FP&A.
𝗧𝗥𝗘𝗡𝗗 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦
Involves examining historical data to identify patterns or trends over time. Simple, cost-effective, but may not account for unexpected changes.
𝗧𝗜𝗠𝗘 𝗦𝗘𝗥𝗜𝗘𝗦 𝗔𝗡𝗔𝗟𝗬𝗦𝗜𝗦
Analyzing a sequence of data at specific time intervals. The primary goal is to identify one-offs, seasonal variations, and cycles to forecast future values.
𝗦𝗘𝗔𝗦𝗢𝗡𝗔𝗟 𝗗𝗘𝗖𝗢𝗠𝗣𝗢𝗦𝗜𝗧𝗜𝗢𝗡
Breaks down time series data into seasonal, trend, and residual components. Suitable for data with strong seasonal patterns.
𝗘𝗫𝗣𝗘𝗥𝗧 𝗝𝗨𝗗𝗚𝗠𝗘𝗡𝗧
Using insights, intuition, and experience of individuals or groups with deep knowledge in a specific area to forecast future events or trends.
𝗘𝗫𝗣𝗢𝗡𝗘𝗡𝗧𝗜𝗔𝗟 𝗦𝗠𝗢𝗢𝗧𝗛𝗜𝗡𝗚
A weighted moving average method where more recent data points have a higher influence. Effective for short-term.
𝗥𝗘𝗚𝗥𝗘𝗦𝗦𝗜𝗢𝗡
It models the relationship between a dependent variable and one or more independent variables. Used when a relationship between variables is suspected.
𝗠𝗢𝗡𝗧𝗘 𝗖𝗔𝗥𝗟𝗢 𝗦𝗜𝗠𝗨𝗟𝗔𝗧𝗜𝗢𝗡
A probabilistic forecasting technique that uses random sampling to simulate a range of possible outcomes. Ideal for assessing risk and uncertainty.
𝗠𝗢𝗩𝗜𝗡𝗚 𝗔𝗩𝗘𝗥𝗔𝗚𝗘𝗦
This method smooths out fluctuations in data by averaging past data points over a specific period. Ideal for short-term forecasting.
𝗠𝗔𝗖𝗛𝗜𝗡𝗘 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚
Uses algorithms that learn from data to predict future outcomes. Can identify non-linear relationships and complex patterns.
𝗛𝗜𝗦𝗧𝗢𝗥𝗜𝗖𝗔𝗟 𝗚𝗥𝗢𝗪𝗧𝗛 𝗥𝗔𝗧𝗘𝗦
Involve using the growth rate of a particular variable (e.g., revenue, sales, market size) from past periods as a basis for forecasting future growth.
