*please make substantive discussion post response to this*

When forecasting a file management system company called LightSpeed, the most efficient forecasting method would be quantitative. This model would be most appropriate because it relies on hard data, which uses historical and future data. A future forecast can be determined since it is based on numerical data that functions from historical data through pattern recognition and causal variables.

Time-Series forecasts may also observe actions leading to final action by evaluating behaviors like trends, seasonality, cycles, irregular variations, and random variations. Quantitative forecasting models are compared against each other by evaluating their accuracy performance measures.

Some typical issues quantitative forecasts encounter are incorporating soft information like human components, biased opinions, and hunches. Issues are not taken as seriously when quantitative models are used because they are complex and impossible to quantify since quantitative methods examine objective or complex data.