Implement of Fuzzy Logic system using table lookup scheme by Java language to predict time series data

2012 ◽  
Author(s):  
J. Kokes ◽  
N.B Nghien
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Table Lookup ◽  
Java Language ◽  
Logic System

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

A conceptual model for automation of product dynamic pricing and sales promotion for a retail organization

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 490-507 ◽  
Author(s):  
Anup Kumar
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Data ◽  
Lot Sizing ◽  
Product Category ◽  
Sales Promotion ◽  
Series Data ◽  
Valuable Insight ◽  
Content Type ◽  
Sales Forecast

Purpose – The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. A modest attempt has been made in the study to capture the relationship between the sales promotion, price discount and the batch procurement strategy of a particular product category to maximize sales volume and profitability. Design/methodology/approach – Time series data relating to sales have been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products that considerably impact the sales promotion and intelligent pricing decisions. A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. Findings – The model captures the lag effect of sales promotion and price discounting strategy; other strategies have been formulated based upon the sales forecast that was done for taking the lot sizing decisions regarding procurement of products in the selected category. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact. Research limitations/implications – There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories. Originality/value – The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

scholarly journals A Novel Method for a COVID-19 Classification of Countries Based on an Intelligent Fuzzy Fractal Approach

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fractal Dimension ◽  
Fuzzy System ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Series Data ◽  
Theoretical Concepts ◽  
Fractal Approach

We outline in this article a hybrid intelligent fuzzy fractal approach for classification of countries based on a mixture of fractal theoretical concepts and fuzzy logic mathematical constructs. The mathematical definition of the fractal dimension provides a way to estimate the complexity of the non-linear dynamic behavior exhibited by the time series of the countries. Fuzzy logic offers a way to represent and handle the inherent uncertainty of the classification problem. The hybrid intelligent approach is composed of a fuzzy system formed by a set of fuzzy rules that uses the fractal dimensions of the data as inputs and produce as a final output the classification of countries. The hybrid approach calculations are based on the COVID-19 data of confirmed and death cases. The main contribution is the proposed hybrid approach composed of the fractal dimension definition and fuzzy logic concepts for achieving an accurate classification of countries based on the complexity of the COVID-19 time series data. Publicly available datasets of 11 countries have been the basis to construct the fuzzy system and 15 different countries were considered in the validation of the proposed classification approach. Simulation results show that a classification accuracy over 93% can be achieved, which can be considered good for this complex problem.

scholarly journals An Interval Type-2 Fuzzy Logic System for Stock Index Forecasting Based on Fuzzy Time Series and a Fuzzy Logical Relationship Map

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 69107-69119 ◽  
Author(s):  
Joe-Air Jiang ◽  
Chih-Hao Syue ◽  
Chien-Hao Wang ◽  
Jen-Cheng Wang ◽  
Jiann-Shing Shieh
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Fuzzy Logic System ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
Logical Relationship ◽  
Interval Type ◽  
Stock Index Forecasting ◽  
Logic System

Modeling of product sales promotion and price discounting strategy using fuzzy logic in a retail organization

2016 ◽  
Vol 116 (8) ◽  
pp. 1418-1444 ◽  
Author(s):  
Anup Kumar ◽  
Amit Adlakha ◽  
Kampan Mukherjee
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Data ◽  
Product Category ◽  
Sales Promotion ◽  
Series Data ◽  
Research Approach ◽  
Valuable Insight ◽  
Content Type ◽  
Product Sales

Purpose The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. Design/methodology/approach Time series data relating to sales has been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products and selection of suppliers. A hybrid model has been proposed and explained with a hypothetical case, which considerably impacts the sales promotion and intelligent pricing decisions. Findings A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. The model imitates sales promotion and price discounting strategy. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact. Research limitations/implications There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories. Originality/value The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

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