Difference between time series and causal forecasting
Answers
As the casual model is broader in the number of factors that are analyzed when determining the outcome of the forecasting exercise. This method is useful when you need to apply several different components to the analysis in order to receive a well-rounded forecast. The time-series model uses time to determine the outcome of the forecasting requirement. The time-series method uses historical data over a set period to determine the result of the forecasting. This is useful in a more limited scenario that is basic in nature.The casual model would be useful in determining the forecast of a bonus system in which several different factors are applied to the final value being distributed. The time-series modelwould be useful in determining traffic levels in a retail store based solely on time of day and no any other factors such as seasonal elements or other data.What are some of the problems and drawbacks of the moving average forecasting model?The average forecasting model assumes that the same forecasting will apply from period to period because it does not take historical data into account. The data is not probable due to this method of computing in a lot of scenarios. This also leads to a high risk of errors in the display of moving trends over any given period of time.How do you determine how many observations to average in a moving average model?. You must utilize the average from the first set of numbers to create a second set and so on. This method continues until the period being measured is complete .How do you determine the weighting to use in a weighted moving average model?.The weighting are determined by applying the average of the quantities being utilized, which becomes the weight, together to get an overall average value. Each of the quantities being utilized is assigned a weight, which is more or less based on the time period of the data. The more recent data will be weight more heavily than historical data.