The classical method of time series decomposition originated in the s and was widely used until the s. 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. Observations that are nearby in time are also likely to be close in value.

### Advantages and Disadvantages of the Simple Moving Average (SMA)?

Therefore, the average eliminates some of the randomness in the data, leaving a smooth trend-cycle component. Figure 6. For example, consider Figure 6.

The data are also shown in Table 6. In the last column of this table, a moving average of order 5 is shown, providing an estimate of the trend-cycle.

The first value in this column is the average of the first five observations — ; the second value in the 5-MA column is the average of the values for —; and so on. Each value in the 5-MA column is the average of the observations in the five year window centred on the corresponding year.

In the notation of Equation 6. This is easily computed using. There are no values for either the first two years or the last two years, because we do not have two observations on either side.

Later we will use more sophisticated methods of trend-cycle estimation which do allow estimates near the endpoints. To see what the trend-cycle estimate looks like, we plot it along with the original data in Figure 6.

Notice that the trend-cycle in red is smoother than the original data and captures the main movement of the time series without all of the minor fluctuations. The order of the moving average determines the smoothness of the trend-cycle estimate. In general, a larger order means a smoother curve. Simple moving averages such as these are usually of an odd order e. It is possible to apply a moving average to a moving average.

One reason for doing this is to make an even-order moving average symmetric. For example, we might take a moving average of order 4, and then apply another moving average of order 2 to the results.A simple moving average SMA is a chart indicator that helps traders see trends and identify key price points for a stock, commodity, forex pair, exchange traded fund, or futures contract.

The indicator is computed as an average of prices over a specific period of time, such as 20, 50, or days. Critics argue that a simple average gives too much weight to old data, which are deemed to be less significant.

Therefore, many traders prefer to use an exponential moving average EMA instead. The SMA is a straightforward calculation because the indicator is simply the average price over a chosen time period. As each new day is added, an old one drops off. A simple moving average provides insight into trends and potential support or resistance areas.

The SMA typically trends higher when prices move up and lower as prices turn to the downside. In an uptrend, a simple moving average often appears below price and, when price falls toward the SMA but not below itthe average identifies a potential area of support. On the other hand, when the simple moving average is above price in a downtrend, the SMA slopes downward and sometimes appears as an area of resistance.

In contrast to the simple moving average, the exponential moving average is computed in a manner that gives greater weighting to the most recent days. That is, the EMA takes a simple moving average and uses a multiplier so that more recent data is assigned more importance. The main advantage of the SMA is that it offers a smoothed line, less prone to whipsawing up and down in response to slight, temporary price swings back and forth.

The SMA is often favored by traders or analysts operating on longer time frames, such as daily or weekly charts. The advantage of the exponential moving average is that by being weighted to the most recent price changes, it responds more quickly to price changes than the SMA does.

This is particularly helpful to traders attempting to trade intraday swing highs and lows since the EMA signals trend change more rapidly than the SMA does.

The concurrent disadvantage of the greater sensitivity of the EMA is that it is more vulnerable to false signals and getting whipsawed back and forth. The EMA is commonly used by intraday traders who are trading on shorter time frames, such as the minute or hourly charts. The advantage of the simple moving average is that the indicator is smoothed and, compared to the EMA, less prone to a lot of false signals.

The drawback is that some of the data used to compute the moving average might be old or stale. Since neither average is inherently superior, the question of which one to use is typically settled by the user's trading style or analytical frame of reference.

Technical Analysis Basic Education. Trading Strategies. Your Money. Personal Finance. Your Practice.Demand Forecasting facilitates critical business activities like budgeting, financial planning, sales and marketing plans, raw material planning, production planning, risk assessment and formulating mitigation plans. Visibility and transparency of raw material demand improve supplier relations and empowers Purchasing Managers to negotiate favorable terms for their companies.

This leads to improved capacity utilization and judicious allocation of manufacturing resources. This reduces the Bullwhip effect across the Supply Chain, leading to optimization of inventory levels and reduction in stock-out or over-stocking situations.

Distribution and Logistics Managers are enabled to balance inventory across the network and negotiate favorable terms with Transporters. This drives synchronized raw material, manufacturing and inventory planning to support new product launches and most importantly, reducing the risk of obsolescence of discontinued products.

Organizational efficiency, effectiveness, and improvement initiatives can be designed for key areas of the company. In this method the sales forecasting is obtained by taking average of past sales over a desired number of past periods may be years, months or weeks. Extending the moving average to include more periods may increase the smoothening effect but decreases the sensitivity of forecast. A simple moving average is formed by computing the average price of a security over a specific number of periods.

Most moving averages are based on closing prices. The simple moving average SMA calculates an average of the last n prices, where n represents the number of periods for which you want the average:. For examplea four-period SMA with prices of 1. The moving averages as calculated in the preceding part are known as un-weighted because the same weight is assigned to each of the numbers whose average is being ascertained.

Some enterprises base their forecast on a weighted moving average. Let us assume that the number of customers who visit during two weeks interval provides a sound basis for third week forecast and let us further assume that first week is less important than second and consequently we assign weights of 0. The weighted average for 9th week would be. Similarly the weighted moving averages for other weeks are enlisted in the following table:.

A forecast based on weighted moving averages for number of customers. Advantages of the Moving Average Method. The cyclic variations are eliminated. Limitations of the Moving Average Method. This method of sales forecasting is a modification of the moving average method or in better words it IS an improvement over the moving average method of forecasting. This method tries to eliminate the limitations of moving averages and removes the necessity of keeping extensive past data it also tries to remove the irregularities in demand pattern.

This method represents a weightage average of the past observations. In this case most recent observations is assigned the highest weightage which decreases in geometric progression as we move towards the older observations. Since the most recent observations which are likely to reflect more up- to-date information or average of the series are given more weightage so it becomes one of the most accurate statistical method of sales forecasting.

This method keeps a running average of demand and adjusts it for each period in proportion to the difference between the latest actual demand figure and the latest value of the average. When there is no trend in the demand for a product or service, sales are forecasted for the next period, by means of the exponential smoothing method by using the expression.

This method follows the equation. If a is equal to 1.

The application of technique is demonstrated by using data of moving averages method of sales forecasting on page In the application of the method, we would use the value of a as 0. If the actual demand for 3rd week isthe forecast for the 4th week will be.Figure 1 — Simple Moving Average Forecast.

Alternatively, these values can be calculated using the formulas. Note that the chart of forecasted values pred in red smooths out the chart of y values in blue. The higher the value of mthe more smoothing that occurs. Note that we can forecast the next value in the time series cell C19 as Excel Data Analysis Tool : Excel provides the Moving Average data analysis tool to simplify the calculations described above. Fill in the dialog box that appears as shown in Figure 2.

Figure 2 — Moving Average dialog box. The output is shown in columns D and E of Figure 3 along with the chart. Figure 3 — Moving Average data analysis.

In this version of the model, the forecast is shifted left by one time unit. Note that the s. The original approach has the forecasted values lagging behind the original values. Note too that if the Intervals field in Figure 2 is not filled in, it defaults to 3. To use this tool for Example 1, press Ctr-m and select Time Series from the dialog that appears. This brings up the dialog box shown in Figure 4. Figure 4 — Time Series dialog box. Click on the Basic forecasting methods option from the menu and then fill in the dialog box that appears as shown in Figure 5 the values for of Lagsof Seasons and of Forecasts are the defaults.

Figure 5 — Forecasting dialog box. When you click the OK button, the output shown in Figure 6 will appear. Figure 6 — Simple Moving Average data analysis. The value in cell F21 is the forecast at time Note that you can optionally omit the time values from the input i.

In this case, the output will only contain two columns i. Note that the standard error values are the same as for the Excel data analysis tool. You can change the alpha value to get any 1-alpha prediction interval that you choose. I have a doubt. What is the difference between this two ways of applying Moving Averages? The one sided approach is better for forecasting? Alejandro, Moving averages can be used for smoothing as well as for forecasting.

The mathematics is identical.Moving averages are often used to help highlight trends, spot trend reversals, and provide trade signals. There are several different types of moving averages, but they all create a single smooth line that can help show you which direction a price is moving.

For example, a four-period SMA with prices of 1. While knowing how to calculate a simple average is a good skill to have, trading and chart platforms calculate this for you.

Simply select the SMA indicator from the list of charting indicators, apply it to the chart, and adjust the number of periods you want to use.

You typically make adjustments to the indicators in the Settings menu section of a trading platform. On many platforms, you can locate the settings by double-clicking on the indicator itself. The advantage of an SMA is that you know exactly what you are getting. Common SMA values are eight, 20, 50,and For example, if using a period SMA, the current value of the SMA on the chart is the average price over the last periods or price bars.

Due to their different calculations, the indicators appear at different price levels on the chart. In other words, the formula gives recent prices more weight than past prices. For example, a four-period EMA with prices of 1.

Select the EMA from the indicator list on a charting platform and apply it to your chart. Go into the settings and adjust how many periods the indicator should calculate, such as 15, 50, or periods.

This takes place because the EMA formula gives more weight to recent prices, and less weight to prices that occurred in the past. The weighted moving average WMA gives you a weighted average of the last n prices, where the weighting decreases with each previous price.

### Simple Moving Average

WMAs can have different weights assigned based on the number of periods used in the calculation. The "10" in that scenario is a randomly chosen number. For the following example, assume prices of 90, 89, 88, 89, with the most recent price first. The most recent price points are usually given more weight, but it could also work the other way, where you give historical prices more weight. Moving averages can be used for both analysis and trading signals.

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