Stat2 Building Models For A World Of Data 19.pdf

Stat2 Building Models For A World Of Data 19.pdf

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Additionally, prior to the outbreak of COVID-19, [24] criticized the use of linear regression as an approach to obtaining forecasts with Big Data. [24] notes that the forecasts obtained using linear regression are not accurate and this method is most suitable for forecasting static data. [24] further noted that non-linear methods like Generalized Linear Models (GLM) are more appropriate for forecasting with Big Data, as these methods are able to handle non-linearity. This view is supported by [87] who notes that linear models are not suitable for forecasting with Big Data as they are unable to handle nonlinearity.

Indeed, various researchers in the field of Economics have stressed the importance of having skillset that could be applicable to this field, as the existing approaches used by researchers to obtain forecasts using traditional methods are not compatible with the new advances in Big Data forecasting. For example, [23] states that the models used to obtain forecasts from traditional data are not applicable to Big Data forecasting as the ability to handle nonlinearity in forecasting is lacking in many of these models. More importantly, [23] notes that a change in the methods that are used to forecast is required to the methods used to obtain forecasts from Big Data, as the forecasts obtained from Big Data is different from traditional forecasts.

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