A moving average model is used for forecasting future values, while moving average smoothing is used for estimating the trend-cycle of past values. Figure 9.6: Two examples of data from moving average models with different parameters. Left: MA (1) with \ (y_t = 20 + \varepsilon_t + 0.8\varepsilon_ {t-1}).
Holt, C. C. (1957). Forecasting seasonals and trends by exponentially weighted averages (ONR Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh USA. Reprinted in the International Journal of Forecasting, 2004. Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6 (3), 324–342.
Bitcoin Magazine: Forecasting Bitcoin Price Cycle Peak with the 200-Week Moving Average
Seeking Alpha: SPY Is A Buy According To The 200-Day Simple Moving Average
The 200-day simple moving average is a reliable indicator for determining a bullish or bearish bias in SPY. The 200-day SMA strategy has outperformed a buy and hold approach since 2000, especially ...
Seeking Alpha: SPY Is A Buy According To The 200-Day Simple Moving Average (Technical Analysis)
SPY Is A Buy According To The 200-Day Simple Moving Average (Technical Analysis)
The classical method of time series decomposition originated in the 1920s and was widely used until the 1950s. It still forms the basis of many time series decomposition methods, so it is important to understand how it works. The first step in a classical decomposition is to use a moving average method to estimate the trend-cycle, so we begin by discussing moving averages.
Some forecasting methods are extremely simple and surprisingly effective. We will use four simple forecasting methods as benchmarks throughout this book. To illustrate them, we will use quarterly Australian clay brick production between 1970 and 2004.