SPSS Forecasting add-on Module and Predictive Modeling
OL-SPSS Forecasting and Predictive Modeling can play a major role in driving a company’s success. It helps to reduce uncertainty and can help anticipate changes in the market. When done properly forecasting can be the differentiator between success and failure. OL-SPSS Forecasting*, is a time series add on module, comprising of methods that analyse time series data that extract meaningful statistics that use time series forecasting models to predict future values based on previously observed values.
OL-SPSS Forecasting add-on Module with Time Series Analysis
Unlike regression techniques, in time series each of these cases are related to each other, as they represent the same phenomena over time. For this reason, the time factor is a predictor of the dependent variable. In other words, in time series analysis, the past provides a model for the future.
Risk and uncertainty are central to forecasting and prediction (Wheelwright, Hyndman, & Makridakis, 1998), it is generally considered good practice to indicate the degree of uncertainty attaching to the forecasts. The data must be up to date for the forecast to be as accurate as possible.
The forecast error (also known as a residual) is the difference between the actual value and the forecast value for the corresponding period. A good forecast will yield residuals that are uncorrelated and have zero mean. If there are correlations between residual values, then there will be information left in the residuals which should be used in computing forecasts. If the residuals have a mean other than zero, then the forecasts are biased.
Examples of time series forecasting are:
• Predicting changes to the market, forces that might affect market share.
• Forecasting customer behaviour, average customer sales, sales fluctuations.
• Predicting and estimating business resources; such as, how much to spend on advertising, what is driving or hurting sales.
Rather than manually defining the parameters, OL-SPSS Forecasting* advanced functionality includes:
• Expert Modeler: This automatically identifies and estimates the best-fitting ARIMA or exponential smoothing model for one or more dependent variable series.
• Seasonal Decomposition: This procedure decomposes a time series into a seasonal component, a combined trend and cycle component, and an “error “component.
• Spectral Plots: This procedure is used to identify periodic behavior in time series.
OL-SPSS Forecasting provides advanced capabilities that enable both novice and experienced users to quickly develop reliable forecasts using time-series data. This tool can be used almost in every single industry and any size of company: Fisheries, Education, Agriculture, Human Resources, Logistics, Energy, Manufacturing, Healthcare, Retail, Telecommunications, Insurance, Finance, Banking and many more.
*Powered by IBM SPSS Statistics Base & Forecasting.
If you feel that OL-SPSS Forecasting Solution is something what your organization need We also provide Training how to work with OL-SPSS Forecasting Add-on Module.