Forecasting means prediction for future. In reference to global marketing practices, the estimations related to the future interest variables, probable risks, rate of market consumption, etc. for any organisation is noted as forecasting (Ellis, 2010). As noted by Ellis (2010) uncertainty and risk management approaches of a company are all set as per the forecasts made by the analysts of the same field.

The most common types of forecasting are based on qualitative analyses and the quantitative assessments. Qualitative remains subjective in natures and assesses the consumer behaviours for forecasting. On the other hand, quantitative follows statistical analytical formulations for forecasting.

The predictions made through average approach consider the values of the future in accordance to the mean attained from past data. Then there is the naïve kind of forecasting that is very cost-effective and is applicable for financial data for time series. Forecasts made through naïve approach assess the economic patterns that are in general hard to predict appropriately.

A popular type of forecasting gets initiated by the drift method, where the amount is subject to change in course of time or drift to be precise. It is depicted by drawing the graph of fall and development and thereby extrapolating the forecasting act. Similar trend gets followed by seasonal naïve method, but on a periodic basis.

The time series assessment process for forecastinggets initiated on the basis of historical data like Autoregressive Moving Average (or the ARMA), Weighted Moving Average, Exponential Smoothing, etc. then there is the econometric forecasting type that is a casual way of forecasting. It follows the regular seasonal variations, along with customary probabilities as followed by the market. This type comprises analysis through regression and autoregressive moving average.

Judgmental type of initiating the forecasting process comprises of the intuitiveness related to subjective probability as well as opinionated statements. Delphi method, scenario building, etc., are some of its in core applicable tools for forecasting. In the contemporary scenario, the artificial intelligence is gaining a stronger grip, whereby data mining, machines to support vector, etc are used at large. 

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