Moving Average Sample Clauses
Moving Average. A moving average (MA) is a price average calculated over a specified period of time. For example, a 10-bar moving average includes the prices of the last ten bars on a chart in its calculation. It is one of the most commonly used trend-following indicators. The Moving Average indicator in eASCTrend is calculated by one of four methods (labeled 0, 1, 2, or 3) depending on the Type of analysis you select. The default for Type is 0, and the default value for Length is 10 bars. Type = 0 calculate using the Closing price Type = 1 calculate using the Opening price Type = 2 calculate using the High price Type = 3 calculate using the Low price MA = (C1 + C2 + ….+ Cn) / N Where, MA = Value of Moving Average C1, C2,…Cn = Close prices of last N days (bars) N = Length to average A moving average (MA) is the average of a market's closing prices over a defined period of time. For example, a 10-day moving average is an average of closing prices over a 10-day period. Moving averages will not indicate an imminent trend change, but will help you determine if an existing trend is continuing by measuring the current direction, or help to confirm that a trend reversal has taken place. A moving average is similar to a trend line except that the moving average curves with the movement of price. It can also provide support and resistance levels.
Moving Average. In this section we will implement moving average forecasting method. In this method, the estimations are based on the last n period observations. We can decide ourselves the best appropriate period for moving average: 2-period, 3 period, etc. Moving average forecasting method is quite accurate over short time period. The main advantages of the method are simplicity, cheap to run, gives good accuracy but it doesn’t work well when there is a trend or seasonal effect in data. The following formulas are used in this method (Winston, 1993): ∑ St −i F = i=1 t N
(1) where Ft St −i = forecast for time period t = actual consumption for period t-i N = number of time periods used in the averaging process We generated forecast for 2010 based on the historical data and implemented 3-period and 5 – period moving averages. In the tables below you can see the obtained results: Forecasted demand Moving average 3- period Moving average 5- period 2010 1593.33 1564.8 Table 9-2 Forecasting results for 2010 by moving average method (.000 of metric tons) Type of Error Moving average 3- period Moving average 5- period MAD 152.53 157.35 MSE 56033.13 53983.35 MAPE 10.45% 9.86% 2200 2000 1800 1600 1400 1200 1000 thousands of metric tons 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Consumption 1880 1995 1230 1260 1260 1300 1301 1812 1388 1656 1620 1550 1610 Forecast_3 1701,6 1495,0 1250,0 1273,3 1287,0 1471,0 1500,3 1618,6 1554,6 1608,6 1593,3 Forecast_5 1525,0 1409,0 1270,2 1386,6 1412,2 1491,4 1555,4 1605,2 1564,8 It is clearly indicated that consumption of crude steel will be decreased but not significantly. In our case due to lack of data we assumed that the forecasted values for 2007 and 2008 as the real values in order to generate forecast for 2010. The results are attached as Appendix C.
Moving Average. Python code: # averaging over 1 second window # make sure window is odd if round(data_inp['freq_sample'].mean()) % 2 != 0: win = int(round(data_inp['freq_sample'].mean())) else: win = int(round(data_inp['freq_sample'].mean()) - 1) data_inp['accx_filt_movg'] = data_inp['accx_filt'].rolling(window=win,, center=True, min_periods=1).mean() # averaging over 2 second window win = int(round(data_inp['freq_sample'].mean()) * 2 - 1) data_inp['accy_filt_movg'] = data_inp['accy_filt'].rolling(window=win, center=True, min_periods=1).mean() data_inp['speed_filt_movg'] = data_inp['speed_filt'].rolling(window=win, center=True, min_periods=1).mean()