Some Important Notes for Time Series Analysis and Forecasting
Analysis of requirements for future based on past spending or usage is known as forecasting. For analysis of data, any form of Time Series methods could be employed. Time Series is a practical analysis of data based on the various trends present or available in statistical analysis of data. Depending on the type of data collected, say in the form of variable data, like for example, population in a country or non-variable over a period of time, like for example, seasons in a country. Depending on the types of data obtained, various time series analysis may be carried out. For sequential data, charts are more representative of information, more descriptive methods are used for correlation, while for seasonally variable data, like those of migration of birds, and so on, seasonal decomposition Time Series Analysis can be utilized.
In automatic forecasting methods, a comparison of the models is done by the computer model and through self-motivation it picks up the best model to be applied in forecasting.
What Are Difficulties Encountered In Time Series Analysis and Forecasting Problems?
The most important problem regarding various Time Series Analysis and Forecasting is the input of faulty data or wrong mode of interpretation of data to be fed into the system for analysis by the students. Thus discrepancy and bias are based on not only the system error, but also personal error of the researcher which forms a vicious cycle and the student will find it difficult to spot the area in which the mistake has revamped the system. Some assumptions on Time Series Analysis are based on the fact that data remains constant. This is not feasible as data can never be maintained constant and is subject to numerous variations.
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