Data Mining and Forecasting Services

Using data mining, companies and organizations can increase the profitability of their businesses by uncovering opportunities and detecting potential risks.

Our data mining and analysis consulting services can help you extract valuable information out of your data by utilizing forecasting modeling (regression and time series analysis). We can analyze your data and provide you with forecasting reports that will suit your need.

Forecasting Models and Project Life Cycle

Forecasting is a component of data mining. It is the process of estimation in unknown situations and is commonly used in discussion of time-series data. Regression models can best be used with time series data to detect trends and seasonalities (even though the models are also useful for cross section data). They can help answer questions such as “What will our sales in the next quarter be?” and “How confident are we in the prediction?” Regression models are also very good for interpolating and extrapolating data in both linear and nonlinear approaches. Our Excel consulting services can provide you with forecast reports by testing your data through various models and implementing the best model that is determined.

We have a team of business analysts, statistical modelers, and IT professionals that utilize tools such as Forecast Pro, SPSS, Statistica, Access, and Excel to perform the analysis.

Our regression models include, but are not limited to:

Here are two examples of forecast plot:

Fig 1. Third (3rd) Order polynomial model

Fig 2. Seasonality (quarterly) model


Linear Regression

Linear regression is used to model the value of a dependent scale variable based on its linear relationship to one or more predictors. It estimates the coefficients of the linear equation, involving one or more independent variables that best predict the value of the dependent variable. For example, you can try to predict a salesperson's total yearly sales (the dependent variable) from independent variables such as age, education, and years of experience.


Nonlinear Regression

Nonlinear regression is a method of finding a nonlinear model of the relationship between the dependent variable and a set of independent variables. Unlike traditional linear regression, which is restricted to estimating linear models, nonlinear regression can estimate models with arbitrary relationships between independent and dependent variables. This is accomplished using iterative estimation algorithms. Note that this procedure is not necessary for simple polynomial models of the form Y = A + BX^2. By defining W = X^2, we get a simple linear model, Y = A + BW, which can be estimated using traditional methods such as the Linear Regression procedure.


Forecasting Analysis

This procedure produces fit/forecast values and residuals for one or more time series, using an algorithm that smoothes out irregular components of time series data. A variety of models differing in trend (none, linear, or exponential) and seasonality (none, additive, or multiplicative) are available.


ARIMA (Box-Jenkins) Example

This procedure estimates non-seasonal and seasonal univariate ARIMA (Autoregressive Integrated Moving Average) models (also known as "Box-Jenkins" models) with or without fixed regressor variables. The procedure produces maximum-likelihood estimates and can process time series with missing observations.


Seasonal Decomposition Example

The Seasonal Decomposition procedure decomposes a series into a seasonal component, a combined trend and cycle component, and an "error" component. The procedure is an implementation of the Census Method I, otherwise known as the ratio-to-moving-average method.


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