Andrew Burao, disaster risk reduction officer at the Municipality of Cabatuan, must forecast expenditures for disaster related activities (such as prevention, rehabilitation, and response). Students are asked to discuss the importance of time series forecasting tools (such as Naïve, Unweighted Moving Average, Weighted Moving Average, and Simple Exponential Smoothing) and identify their pros/cons. They then calculate the 2016 disaster expenditure and 2017 disaster budget using these techniques, along with calculating the Mean Squared Error (MSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) for each forecast.
Catch the FUNDS, if you can!: Forecasting disaster-related expenses
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After reading and discussing the material, students should:
- Identify and contrast various time-series forecasting tools in decision-making.
- Outline Burao's challenges and opportunities in determining how allocate funds in various disaster-related activities.
- Compare budget projections for the year 2017 using various time-series forecasting tools.
- Critique alternative forecasts' levels and select the optimal decision for Burao.