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Today I share Some information about Adjustment of time series data.

Before beginning the actual work of analyzing a time series it is necessary to make certain adjustment in the raw data to removed the unwanted elements of time series. the adjustments may be needed for:

Calendar variations.

Population changes

price changes

comparability

`1. Adjustment of calendar variation:`

in order to eliminate difference caused by calendar variation, some adjustments in that month will be less. the adjustment is made by dividing each monthly total by the number of days in the month to get the daily average for each month.

For example, the month of February has 28 days (29 days), march has 31 days, etc.

`2. Adjustment for population changes:`

The changes in the size of population can turn the comparisons of many things. for instance, National Income may be increasing every year, while per capita income may be decreasing, because of the population growth. If we want to get the per capita income month by month or year by year, then the total national income must be divided by the number of months or the number of years. For the period and by population in each of these time - intervals. This will remove the influence of increasing population.

`3. Adjustment for price Changes:`

The effect in price changes in raw data can be removed by dividing each weekly figure by the cost of living index for that week. it must be followed in the entire series.

`4. Adjustment for comparison Purposes:`

If two or more series are to be compared in time series, they are converted into percentages of a given past period. Then the percentages can be compared easily.

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$ 0.00Habiba22 Adjustment of time series Data. 7 19 0 EXC BOOST Avatar for Foysol_Ahmod6678 Written by Foysol_Ahmod6678 17 Subscribed 1 day ago In community: Open For All(cc80) Today I share Some information about Adjustment of time series data.

Before beginning the actual work of analyzing a time series it is necessary to make certain adjustment in the raw data to removed the unwanted elements of time series. the adjustments may be needed for:

Calendar variations.

Population changes

price changes

comparability

For example, the month of February has 28 days (29 days), march has 31 days, etc.

Adjustment for population changes: The changes in the size of population can turn the comparisons of many things. for instance, National Income may be increasing every year, while per capita income may be decreasing, because of the population growth. If we want to get the per capita income month by month or year by year, then the total national income must be divided by the number of months or the number of years. For the period and by population in each of these time - intervals. This will remove the influence of increasing population.

Adjustment for price Changes: The effect in price changes in raw data can be removed by dividing each weekly figure by the cost of living index for that week. it must be followed in the entire series.

Adjustment for comparison Purposes: If two or more series are to be compared in time series, they are converted into percentages of a given past period. Then the percentages can be compared easily.

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$ 0.00 User's avatar Anowar90 Reply 17 hours ago Time series data analysis is a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals. The data is considered in three types:

Time series data: A set of observations on the values that a variable takes at different times.

Cross-sectional data: Data of one or more variables, collected at the same point in time.

Pooled data: A combination of time series data and cross-sectional data.

Terms and concepts:

Dependence: Dependence refers to the association of two observations with the same variable, at prior time points.

Stationarity: Shows the mean value of the series that remains constant over a time period; if past effects accumulate and the values increase toward infinity, then stationarity is not met.

Differencing: Used to make the series stationary, to De-trend, and to control the auto-correlations; however, some time series analyses do not require differencing and over-differenced series can produce inaccurate estimates.

Specification: May involve the testing of the linear or non-linear relationships of dependent variables by using models such as ARIMA, ARCH, GARCH, VAR, Co-integration, etc.

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