PM Technical Article Podcast: Time Series Analysis and Moving Averages

Описание: #ACCAPM #TimeSeriesAnalysis #PerformanceManagement

Time Series Analysis – ACCA PM Exam Focus

Key exam points for ACCA PM:

• Understand how time series analysis helps identify trends and seasonal patterns
• Use moving averages to smooth out data and reveal the underlying trend
• Distinguish between additive and multiplicative seasonal variations
• Recognise that time series can help improve budgeting and forecasting
• Know how to calculate seasonal variations and adjust forecasts accordingly
• Understand how even vs odd period moving averages affect midpoint placement
• Be aware of the assumptions behind time series, especially that the past predicts the future

Tip: In exam questions, always show clear workings for moving averages, trends, and seasonal variations. Make sure to state any assumptions and adjust forecasts using the correct seasonal pattern.

Example Concepts Covered:

• Identifying a three-month cycle in monthly sales data
• Calculating three-month moving averages and plotting the trend
• Estimating trend increase ($2,000/month) and projecting future sales
• Using additive model: actual – trend = seasonal variation
• Using multiplicative model: actual ÷ trend = seasonal variation %
• Forecasting future sales by applying trend + seasonal adjustments
• Handling even-number period averages by calculating centred averages

Common exam pitfalls:

• Confusing trend with seasonal variation
• Forgetting to centre moving averages when using even-numbered periods
• Applying seasonal variations incorrectly (wrong month or wrong model)
• Ignoring the need for seasonal variations to sum correctly (additive = 0, multiplicative = number of periods)
• Assuming trends continue indefinitely without considering real-world limits
• Misinterpreting the direction or impact of seasonal variation

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