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What advantages as a forecasting tool does exponential smoothing have over moving average?
Wiki User
∙ 12y ago
When implemented digitally, exponential smoothing is easier to implement and more efficient to compute, as it does not require maintaining a history of previous input data values. Furthermore, there are no sudden effects in the output as occurs with a moving average when an outlying data point passes out of the interval over which you are averaging. With exponential smoothing, the effect of the unusual data fades uniformly. (It still has a big impact when it first appears.)
Wiki User
∙ 12y ago
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How do you derive the exponential smoothing factor?
Lets define exponential smoothing first... Exponential smoothing, or exponential moving average, is a running average of a set of observations, where the weight of each observation is inversely exponentially weighted as a function of how old it is. It is a relatively simple thing to do. Given a set of observations O1, O2, O3, ... ON the running exponential moving average A1, A2, A3, ... AN can be calculated in real time, at each time N, with the expression ... AN = AN-1 (1 - X) + ON X ... where X is a weighting factor that determines that amount of smoothing. For instance, if X were zero, then the smoothing is infinite, and O does not contribute at all to A, and if X were one, then smoothing is zero, and A follows O with no smoothing at all. In a more useful example, if X were 0.2, then the smoothing would be five, and A would follow O with a time constant of five iterations, i.e. after five iterations we would be at about 63% of one step change and after 25 iterations we would be at about 95% of one step change. Some people swap the position of X and (1 - X) in the above equation. Its their choice, but the discussion that follows will have to change accordingly. X is the smoothing factor. It is simply the number of iterations that you want for your time constant. If you were to model this as an electronic circuit, for instance, with a capacitor and a resistor, the exponential curve would be in the form ... e-T/RC ... where RC was your time constant. The same thing applies here. If you evaluated the first equation once per second, with an X value of 0.2, you would have a time constant of 5 seconds. If you, on the other hand, evaluated it 100 times per second, with X being 0.002, you would still have a time constant of 5 seconds, but it would much more closely approximate the second equation, which is a continuous equation, rather than a discrete equation. In summary, then, the smoothing factor, or X, is one over the number of iterations that you want to be your time constant.
What is a frequency distribution defined by its average and standard deviation?
The triangular, uniform, binomial, Poisson, geometric, exponential and Gaussian distributions are some that can be so defined. In fact, the Poisson and exponential need only the mean.
Relationship between Exponential and Poisson Distributions?
Poisson distribution shows the probability of a given number of events occurring in a fixed interval of time. Example; if average of 5 cars are passing through in 1 minute. probability of 4 cars passing can be calculated by using Poisson distribution. Exponential distribution shows the probability of waiting times between occurrences of events. If we use the same example; probability of a car coming in next 40 seconds can be calculated by using exponential distribution. -Poisson : probability of x times occurrence -Exponential : probability of waiting times between events.
What is advantages and disadvantages of weighted average?
The advantages of using weighted averages are that it smooths out fluctuations due to statistical outliers. The disadvantage is that this gives a uniformity in the statistics and can make it difficult to project trends.
What are the advantages of weighted average?
More importance can be attached to observations which are either of greater importance, accuracy (lesser variance).
What are forecasting models?
1) forecasting for stationary series A- Moving average B- Exponential Smoothing 2) For Trends A- Regression B- Double Exponential Smoothing 3) for Seasonal Series A- Seasonal factor B- Seasonal Decomposition C- Winters's methode
How do you Calculate Exponential Moving average?
Exponential moving average is a running average of a set of observations, where the weight of each observation is inversely exponentially weighted as a function of how old it is. It is a relatively simple thing to do. Given a set of observations O1, O2, O3, ... ON the running exponential moving average A1, A2, A3, ... AN can be calculated in real time, at each time N, with the expression ... AN = AN-1 (1 - X) + ON X ... where X is a weighting factor that determines that amount of smoothing. For instance, if X were zero, then the smoothing is infinite, and O does not contribute at all to A, and if X were one, then smoothing is zero, and A follows O with no smoothing at all. In a more useful example, if X were 0.2, then the smoothing would be five, and A would follow O with a time constant of five iterations, i.e. after five iterations we would be at about 63% of one step change and after 25 iterations we would be at about 95% of one step change.
How do you derive the exponential smoothing factor?
Lets define exponential smoothing first... Exponential smoothing, or exponential moving average, is a running average of a set of observations, where the weight of each observation is inversely exponentially weighted as a function of how old it is. It is a relatively simple thing to do. Given a set of observations O1, O2, O3, ... ON the running exponential moving average A1, A2, A3, ... AN can be calculated in real time, at each time N, with the expression ... AN = AN-1 (1 - X) + ON X ... where X is a weighting factor that determines that amount of smoothing. For instance, if X were zero, then the smoothing is infinite, and O does not contribute at all to A, and if X were one, then smoothing is zero, and A follows O with no smoothing at all. In a more useful example, if X were 0.2, then the smoothing would be five, and A would follow O with a time constant of five iterations, i.e. after five iterations we would be at about 63% of one step change and after 25 iterations we would be at about 95% of one step change. Some people swap the position of X and (1 - X) in the above equation. Its their choice, but the discussion that follows will have to change accordingly. X is the smoothing factor. It is simply the number of iterations that you want for your time constant. If you were to model this as an electronic circuit, for instance, with a capacitor and a resistor, the exponential curve would be in the form ... e-T/RC ... where RC was your time constant. The same thing applies here. If you evaluated the first equation once per second, with an X value of 0.2, you would have a time constant of 5 seconds. If you, on the other hand, evaluated it 100 times per second, with X being 0.002, you would still have a time constant of 5 seconds, but it would much more closely approximate the second equation, which is a continuous equation, rather than a discrete equation. In summary, then, the smoothing factor, or X, is one over the number of iterations that you want to be your time constant.
What is demand analysis forecasting?
I'll give you the gist of Demand Analysis Forecasting: Demand analysis forecasting is the process estimation of quantity of a product or service that will be demanded by the customer in the future. Demand forecasting is carried out using both, informal methods, like educated guesses or quantitative methods that involve the use of historical data or existing data from the test markets. Demand forecasting helps in the formulation of pricing strategies, estimation of future product capacity and making crucial decisions relating to the entry or exit from new markets. Methods of Demand forecasting: Qualitative Methods: 1. Jury of expert opinion method 2. Delphi Method: *Developed by RAND Corp *Individuals are asked to answer questionnaires in a total of 2 to 3 rounds *The persons involved often maintain anonymity even after the test has been completed. Quantitative Methods: 1. Time series projection methods: *Trend projection method *Exponential smoothing method *Moving average method Casual methods: 1. Chain ratio method 2. Consumption level method 3 End use method 4.Leading indicator method
What is forecasting analysis?
I'll give you the gist of Demand Analysis Forecasting: Demand analysis forecasting is the process estimation of quantity of a product or service that will be demanded by the customer in the future. Demand forecasting is carried out using both, informal methods, like educated guesses or quantitative methods that involve the use of historical data or existing data from the test markets. Demand forecasting helps in the formulation of pricing strategies, estimation of future product capacity and making crucial decisions relating to the entry or exit from new markets. Methods of Demand forecasting: Qualitative Methods: 1. Jury of expert opinion method 2. Delphi Method: *Developed by RAND Corp *Individuals are asked to answer questionnaires in a total of 2 to 3 rounds *The persons involved often maintain anonymity even after the test has been completed. Quantitative Methods: 1. Time series projection methods: *Trend projection method *Exponential smoothing method *Moving average method Casual methods: 1. Chain ratio method 2. Consumption level method 3 End use method 4.Leading indicator method
What tables represent an exponential function. Find the average rate of change for the interval from x 7 to x 8.?
what exponential function is the average rate of change for the interval from x = 7 to x = 8.
Over the history of the human population the average increase rate of populations?
C. exponential
What are some synonyms for exponential?
Some good synonyms for the word "exponential" are, accumulative, declining, depleted, down, fourfold, gathering, graduated, growing, abacus, algorithm, approximation, average, countdown, binomial and deviation.
What is a frequency distribution defined by its average and standard deviation?
The triangular, uniform, binomial, Poisson, geometric, exponential and Gaussian distributions are some that can be so defined. In fact, the Poisson and exponential need only the mean.
What is best forex trend indicator?
The triple exponential average (TRIX) is one of the indicators helping finding the trend.
What is the purpose of exponential averaging technique?
Exponential Averaging - Generates a continuous running average where the most recently sampled spectra have more influence on the average than older ones. This provides a convenient form to examine changing data with the benefit of some averaging to smooth the spectra. The Vibration A to Z on the VibroNurse Website
The method is a forecasting method based on the idea that the weather on a any date will be close to the average of the weather that occurred on that date throughout the years?
its analog
