Moving average represents a time series forecasting technique for the following reasons:

Smoothing Technique: Moving average is used to smooth out short-term fluctuations and highlight longer-term trends or cycles in the data. It calculates the average of a fixed number of past observations and moves forward through the time series data.

Forecasting: This method helps in predicting future values based on the average of past data points. By considering a specified number of previous observations, it provides a simple yet effective way to forecast future trends.

Reduction of Noise: By averaging a number of past observations, the moving average technique reduces the impact of random variations and noise in the data, making it easier to identify underlying trends.

Versatility: Moving averages can be adapted to different periods (e.g., short-term, medium-term, long-term) by adjusting the number of observations included in the average. This flexibility makes it useful for various types of time series data.

Application: It is widely used in various fields, including finance, economics, and supply chain management, for tasks such as inventory forecasting, demand planning, and trend analysis.

References

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.