Data Smoothening: An Interesting Topic to Be Studied in Statistic
Date Smoothening is known as the process of using an algorithm to eradicate noise from a given set of data. One should not mingle data smoothening with curve fitting.It has a lot of benefits in financial market. It is helpful in predicting trend such as securities prices. If you are looking more details about data smoothening, you must try data smoothening assignment help at 24x7assignmenthelp.com. Our website is serving the students with all the help in statistics and various other subjects.
Difference between data smoothening and curve fitting
Curve fitting requires explicit functions whereas only smoothed values are used in data smoothening. A curve fitting aims on getting a close match but data smoothening aims at knowing the idea of relatively slow change of value. Data Smoothening has a tuning parameter, which could be used for controlling the smoothening however, in curve fitting, is capable of adjusting as many numbers of parameters. More detailed differences are available with our academic professionals. You may get in touch with them to get data smoothening homework help.
Different types of algorithms data smoothening
There are various types of mathematical process by which data smoothening can be done. Following are the ways:
- Random
- Random walk
- Simple exponential
- Moving average
- Seasonal exponential
- Linear exponential
Random walk is used to explain the attitude of financial instruments like stock. It is believed by many investors that the past security price and the future price prices of stock are not linked in any manner. So, in random walk smoothening process, it is assumed that future data points will be equal to the last available data point. However, there are fundamental and technical analysts who disagree with the fact. They feel that future data could be extrapolated by checking the past trends. To know more about other types of algorithms, you may like to have data smoothening assignment help.
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