what is the difference between imputation and forecasting? All i know, forecasting is the term used in time series analysis, which means predicting the future value by considering the trend of the historical data. Imputation is a strategy for completing data. I've thought some answers to my own question. But so far, these are what i got:
forecasting is done by considering the trend of the historical data, while imputation sees the data (each of the observation) as random variable, so we should consider the distribution of the history.
The term forecasting used for predicting the future value, while imputation used for predicting the missing value.
forecasting procedure will never be related to unknown data (in this case, any value that hasn't been observed) , while imputation procedure might depends on the unknown data (in this case, unobserved data) (informative drop out).
Best Answer
In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation". Because missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with listwise deletion of cases that have missing values. That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with a probable value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data.
On the other hand,
Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.