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System and method for building a time series model

A time series and model technology, applied in instruments, complex mathematical operations, data processing applications, etc., can solve the problem of time-consuming, unable to cover the true value model order, pattern recognition method can not well determine the seasonal AR and MA orders And other issues

Inactive Publication Date: 2009-07-15
SPSS
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This process can be very time consuming, and the values ​​of I, J, K, L may be too low to cover the true model order
[0010] Although the recognition method is computationally faster than the compensation function method, the pattern recognition method does not perform well in determining the seasonal AR and MA orders

Method used

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  • System and method for building a time series model
  • System and method for building a time series model
  • System and method for building a time series model

Examples

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Effect test

example 1

[0189] Constructing a Univariate ARIMA Model for International Airline Passenger Data

[0190] In this example, the sequence is the monthly total of international airline passengers traveling from January 1949 to December 1960. Such as Figure 4 , where the y-axis represents the number of passengers (in thousands), and the x-axis shows the year and month.

[0191] Box and Jenkins (1976) studied this series and found that a logarithmic transformation was required. They determine a (0,1,1)(0,1,1) model for this log-transformed sequence. Therefore, the model of the log-transformed sequence (0,1,1)(0,1,1) is called the "airline" model. The method of the present invention finds the same model for the monthly total number of international airline passengers as the input time series to be predicted, and "12" as the input seasonal cycle. Figure 5 Predicted values ​​using the model shown along with the input time series are shown. The figure shows future values ​​for a one-year f...

example 2

[0193] Constructing a Multivariate ARIMA Model of Clothing Sales

[0194] Such as Figure 6A and 6B A multivariate ARIMA model constructed to predict men's and women's apparel catalog sales is shown in . The dataset consists of simulated and raw data, and it includes monthly sales of men's and women's clothing by catalog companies from January 1989 to December 1998. There are 5 predictors that could potentially impact sales, including:

[0195] (1) The number of catalogs mailed, referred to as "mails";

[0196] (2) the number of pages in the catalog, referred to as "pages";

[0197] (3) The number of telephone lines used for ordering, referred to as "telephones";

[0198] (4) the amount spent on printing the advertisement, referred to as "printing"; and

[0199] (5) Number of customer service representatives, referred to as "services."

[0200] Other considerations include the strike in June 1995 (the "Strike"), the printing accident in September 1997 (the "Incident") a...

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Abstract

The invention relates to a method and a system for processing data. The method is used for reducing user input interaction quantity in a task of predicting future trends using a computer. The method comprises the steps as follows: inputting separated data values of time series and season circulation length in a computer; inputting at least one type of a prediction factor, interference and event represented by digital numeric values in a computer; determining the ARIMA exponent number of time series; removing a prediction factor with at least one lost value; constructing an initial multi-element ARIMA model of time series according to the ARIMA exponent number of time series, interference and accident and residual prediction factors; correcting the initial multi-element ARIMA model according to an iterative model assessment result, diagnostic check and residual self correlation function / partial self correlation function; establishing a multi-element ARIMA model of time series so as to reduce the quantity of the user input interactions; and predicting the future trends using the multi-element ARIMA model established by the computer.

Description

[0001] This application is a divisional application of a Chinese patent application with an application date of November 8, 2001, an application number of 01821857.1, and an invention title of "System and Method for Constructing a Time Series Model". technical field [0002] The present invention relates to methods and computer systems for specifying models for time series. Background technique [0003] There is a strong desire to be able to accurately model and predict events, especially in today's business environment. Accurate modeling will help people predict future events, leading to better decisions and better grades. Because reliable information about future trends is so valuable, many organizations spend considerable human and financial resources trying to predict future trends and analyze the likely outcomes of those trends. A fundamental purpose of forecasting is to reduce risk and uncertainty. Business decisions rely on forecasts. Therefore, forecasting is an e...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/00G06F17/18G06F17/10G06F17/17G06Q10/06
CPCG06F17/18G06Q10/06
Inventor 方东平瑞·S·蔡
Owner SPSS
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