WHY PREDICT-MARKET ?
Only PREDICT-MARKET Prediction Service
* Offers accurate market analysis, based on datamining, on a worldwide overview
* Offers targeted predictions on selected geographic zones, markets, stocks or companies
* Change your investment strategies from risk to safety
PREDICT-MARKET IS #1 IN PREDICTIVE DATA MINING
The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has the most direct business applications.
Predictive Data Mining is usually applied to identify a statistical or neural network model or set of models that can be used to predict some response of interest. The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identification with validation/verification, and (3) deployment (i.e., the application of the model to new data in order to generate predictions).
PREDICT-MARKET.BIZ predictive analysis integrate :
**# On-Line Analytic Processing (OLAP)**
On-Line Analytic Processing - OLAP (or Fast Analysis of Shared Multidimensional Information - FASMI) or multidimensional databases allows to generate on-line descriptive or comparative summaries ("views") of data and other analytic queries.
**# Computational EDA techniques**
Computational exploratory data analysis methods include both simple basic statistics and more advanced, designated multivariate exploratory techniques designed to identify patterns in multivariate data sets.
**# Statistical exploratory methods.**
The basic statistical exploratory methods include such techniques as examining distributions of variables (e.g., to identify highly skewed or non-normal, such as bi-modal patterns), reviewing large correlation matrices for coefficients that meet certain thresholds (see example above), or examining multi-way frequency tables (e.g., "slice by slice" systematically reviewing combinations of levels of control variables).
**# Multivariate exploratory techniques.**
Multivariate exploratory techniques designed specifically to identify patterns in multivariate (or univariate, such as sequences of measurements) data sets include: Cluster Analysis, Factor Analysis, Discriminant Function Analysis, Multidimensional Scaling, Log-linear Analysis, Canonical Correlation, Stepwise Linear and Nonlinear (e.g., Logit) Regression, Correspondence Analysis, Time Series Analysis, and Classification Trees.
**# Neural Networks**
Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data. |