Moving Average Forecasting For Avocado Assignments ✓ Solved

Moving Average Forecasting Avocadosassignmentafter Viewing The Vide

Complete a spreadsheet related to moving average forecasting for avocado prices and sales, including calculations for 5-week moving averages, forecast errors, and Mean Absolute Percentage Error (MAPE). Create three specified line graphs visualizing avocado prices and sales data for 2020, with appropriate titles, axis labels, and legends. Answer additional questions on a second tab of the spreadsheet. Submit the completed Excel file for evaluation.

Sample Paper For Above instruction

The increasing demand for avocados has led to heightened interest in accurate forecasting methods to predict prices and sales volumes. Moving average forecasting, particularly the 5-week moving average, provides a straightforward approach to analyze trends over time. This paper explores the methods and applications of moving average forecasting to avocado market data, including calculations, error measurement, and data visualization through graphs.

Introduction

Forecasting is vital for stakeholders in the agricultural sector to make informed decisions regarding production, inventory management, and pricing strategies. Among various forecasting techniques, the moving average method stands out due to its simplicity and effectiveness in smoothing random fluctuations in time series data. Specifically, the 5-week moving average considers the average of the most recent five weeks, providing a responsive yet stable prediction of future values.

Methodology

The process begins with collecting weekly data on avocado prices—both conventional and organic—as well as sales volume. The 5-week moving average forecast is computed by averaging the actual data points from the previous five weeks. This calculation is performed iteratively for each subsequent week, producing forecasted values beyond the initial five-week period. Errors between actual and forecasted values are then calculated, including the weekly forecast error and the absolute percentage error, which enables the computation of MAPE as a measure of forecast accuracy.

Data Calculation and Analysis

The actual data from February 6, 2020, through December 31, 2020, serves as the basis for the analysis. For each week, the forecasted value for prices and sales is obtained by averaging the preceding five weeks. These forecasts are then compared to the actual observed values to determine the forecast errors, which are crucial for assessing the model's accuracy. The MAPE provides an overall error metric, with lower values indicating more precise forecasts.

Graphical Representation

To visualize trends and the accuracy of forecasts, three key graphs are developed:

  1. Weekly conventional and organic avocado prices for 2020, illustrating price fluctuations over time.
  2. Weekly avocado prices and sales for 2020, with dual axes to compare these two variables directly.
  3. Similarly, weekly organic avocado prices and sales are plotted, highlighting the relationship between price trends and sales volumes in the organic segment.

Proper labels, titles, legends, and axis descriptions are incorporated to enhance readability and interpretability. These visualizations facilitate the identification of seasonal patterns and potential discrepancies between forecasted and actual data.

Conclusion

Applying the 5-week moving average method to avocado market data demonstrates its utility in identifying underlying trends amidst short-term fluctuations. While the forecasts tend to smooth out irregularities, accuracy assessments via forecast errors and MAPE help refine the approach. Accurate forecasting supports better decision-making in pricing strategies, supply chain management, and market planning for avocado producers and retailers.

References

  • Chatfield, C. (2000). The Analysis of Time Series: An Introduction. CRC Press.
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
  • Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting Methods and Applications. John Wiley & Sons.
  • Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting. Wiley.
  • Orr, D. (2010). Business Forecasting. South-Western College Pub.
  • Robinson, J. P., & Iyer, R. (2010). Data Analysis for Time Series Forecasting. Journal of Business & Economic Perspectives, 2(1), 45–62.
  • Chatfield, C. (2016). The Analysis of Time Series: An Introduction, Sixth Edition. Chapman and Hall/CRC.
  • Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted moving averages. Office of Naval Research R&M, 52, 1–19.
  • Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, Conclusions & Implications. International Journal of Forecasting, 16(4), 451–476.
  • Goyal, R., & Gopal, M. (2010). Application of Moving Average Method in Forecasting of Agricultural Commodity Prices. International Journal of Advanced Research in Computer Science and Software Engineering, 1(4), 166–172.

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