datax forecast

Market Forecast using DataX™
-- A Fuzzy Logic Approach to Time Series Analysis
(Click here for a DataX™ Catalog)

The issue of forecast (prediction)
Databases, datamart, and data warehouses are increasingly used in business, finance, engineering, medicine and many other fields. They contain tons of history data which can be utilized to forecast (predict) the future trend of the various systems, such as market forecasting, stock price prediction, and risk management. According to an IBM research, only 7% of the corporate data are ever used and most of them are untouched.

Fuzzy logic combined with Fourier time series analysis ?
Fuzzy logic works in the gray area between "Yes" and "No." Fourier analysis is used for component analysis or cyclic analysis of a deterministic (certain) physical system, such as the harmonic analysis of a speech signal using a HP spectrometer. But most practical systems involve uncertain factors which call for special methodologies, such as fuzzy logic, to model and analyze for better results. Examples include fluctuations of consumer prices, stock prices, and sales revenue. Scientists at Zaptron Systems, Inc. have invented advanced technologies to combine fuzzy logic with Fourier analysis and time series forecasting, and have built it into the software tool base of DataX™. This theory has been validated with real-world data, and the average prediction accuracy is 97%.

Application Example One (real-world data):
Problem description - We were given data of 5 consumer prices (chicken, cucumber, eggs, beef and pork) between Jan. 1994 and Jan. 1996 for one city in Asia . The original prices are shown in the upper-left picture below. The goal was to use DataX™ to build computer models that can accurately predict these 5 prices.

Solution - DataX was used to perform the following tasks: (see picture below)

1) Principal component analysis on each price data, giving up to 3 principal harmonics. The upper-right picture below shows the first harmonic of each data. The picture shows that the period of the change in the price

	a) for chicken (blue) is 11 months
	(b) for cucumber (green) is 13 months
	c) for eggs (red) is 18 months,
	(d) for beef (light blue) is 36 months (curve is edged due to lack of consumption data), and
	(e) for pork (yellow) is 6 months

2) Model Building (wave analysis)- Based on the given price data (original data), DataX was used to build a computer model for each price data separately (assuming no relationship between these 5 price data). Then these models were used to re-generate price data to validate the models. If the re-generated data (simulated data) are close to those given, then the model thus built is acceptable and can be used for prediction of future prices. The lower-left picture below shows the original data and the re-generated data for chicken. They are very close, with error (red) near 0.

3) Forecast - Prediction: The above models built from using DataX for the 5 consumer prices were used to predict the future prices, and the prediction results are shown as 5 color curves in the lower-right picture below.. The real-world data for July 1995 to March 1996 are also displayed as black curves in the same picture. It is shown that DataX™ can produce very good prediction, with average prediction error less than 3%.

Explanation of the 4 pictures shown below:

	Upper-left - original data for 5 commodity prices for 5 years (5 data from from history)
	Upper-right - principle component analysis (first harmonic for each of the 5 data)
	Lower-left - build a model for each data and reconstruct data with the models (green), error < 3%
	Lower-right - forecasted prices (color) vs. real-world prices (black) of the 5 types of goods.

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