scholarly journals Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 768 ◽  
Author(s):  
Rachna Jain ◽  
Nikita Jain ◽  
Shivani Kapania ◽  
Le Son
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Crop Yields ◽  
Wheat Yield ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Yield Production ◽  
Fuzzy Partitioning ◽  
Predicted Values ◽  
Mean Function

Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

scholarly journals Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

2018 ◽  
Vol 10 (10) ◽  
pp. 1659 ◽  
Author(s):  
Inbal Becker-Reshef ◽  
Belen Franch ◽  
Brian Barker ◽  
Emilie Murphy ◽  
Andres Santamaria-Artigas ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Series Data ◽  
Crop Yields ◽  
Wheat Yield ◽  
Series Data ◽  
Crop Type ◽  
Winter Wheat Yield

Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data.

A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

2018 ◽  
Vol 14 (01) ◽  
pp. 91-111 ◽  
Author(s):  
Abhishekh ◽  
Surendra Singh Gautam ◽  
S. R. Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stock Exchange ◽  
Market Price ◽  
Intuitionistic Fuzzy Set ◽  
Score Function ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

PERBANDINGAN FUZZY TIME SERIES DENGAN METODE CHEN DAN METODE S. R. SINGH (Studi Kasus : Nilai Impor di Jawa Tengah Periode Januari 2014 – Desember 2019)

2020 ◽  
Vol 9 (3) ◽  
pp. 306-315
Author(s):  
Febyani Rachim ◽  
Tarno Tarno ◽  
Sugito Sugito
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Series Method ◽  
Fuzzy Variables ◽  
Central Java ◽  
Time Series Method ◽  
Fuzzy Logical

Import is one of the efforts of an area to meet the needs of its population in order to stabilize prices and maintain stock availability. The value of imports in Central Java throughout 2016 amounted to 8811.05 Million US Dollars. The value of imports in Central Java is the top 10 in all provinces in Indonesia with a percentage of 6.50%. Import data in Central Java is included in the time series data category. To maintain the stability of imports in Central Java, it is deemed necessary to make a plan based on a statistical model. One of the time series models that can be applied is the fuzzy time series model with the Chen method approach and the S. R. Singh method because the method is suitable for cyclical patterned data with monthly time periods such as Import data in Central Java. Important concepts in the preparation of the model are fuzzy sets, membership functions, set basic operators, fuzzy variables, universe sets and domains. The fuzzy time series modeling procedure is carried out through several stages, namely the determination of universe discourse which is divided into several intervals, then defines the fuzzy set so that it can be performed fuzzification. After that the fuzzy logical relations and fuzzy logical group relations are determined. The accuracy calculation in both methods uses symmetric Mean Absolute Percentage Error (sMAPE). In this study the sMAPE value obtained in the Fuzzy Time Series Chen method of 10.95% means that it shows good forecasting ability. While the sMAPE value on the Fuzzy Time Series method of S. R. Singh method by 5.50% shows very good forecasting ability. It can be concluded that the sMAPE value in the S. R. Singh fuzzy time series method is better than the Chen method.Keywords: Import value, fuzzy time series , Chen, S. R. Singh, sMAPE

scholarly journals Prediksi Penjualan Produk Promo PT. Unilever, Tbk Menggunakan Metode Fuzzy Time Series

2020 ◽  
Vol 6 (2) ◽  
pp. 51-57
Author(s):  
Yehoshua Yehoshua ◽  
Kustanto Kustanto ◽  
Retno Tri Vulandari
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Literature Study ◽  
Price Discounts ◽  
Input Design ◽  
Time Series Method ◽  
Item Data ◽  
Body Care

PT. Unilever is a multinational company headquartered in Rotterdam, the Netherlands (under the name Unilever N.V.), London, England (under the name Unilever pic.) And in Indonesia has a subsidiary, PT. Unilever, Tbk was established on December 5, 1933. Unilever produces food, drinks, cleaners, and also body care. Unilever is the third largest producer of household goods in the world, if based on the amount of revenue in 2012, behind P & G and Nestle. In forecasting products, it is often influenced by the sale of these products because there are also changes in sales for each period. Usually there is an increase in sales of these products which, among other things, is caused by price discounts, new products, one free one to buy promo, or a saving package from Unilever or from a rival company. Data collection method used by the author is a method of observation or directly observing the process of transmission, interview methods and literature study methods. While the method for processing data uses fuzzy time series algorithms, context diagrams, data flow diagrams, HIPO, relational diagram entities, data dictionary design, input design, output design, relation diagrams between tables, system implementation and testing. The method for implementation uses vb.net and Mysql. The results of this thesis are a system for calculating the forecasting amount of sales or sales of promo products for the following year. From this system, information on store data, item data, sales year history data, and forecasting data from fuzzy time series data will be displayed.. From rinso goods promotion data which have been calculated using fuzzy time series method which get MAPE value equal to 3,2%, so sales data for category of goods will experience increase based on calculation equal to 3,2%.

scholarly journals Fuzzy Time-Series Model of Electric Power Consumption

2000 ◽  
Vol 4 (3) ◽  
pp. 188-194 ◽  
Author(s):  
Kazuhiro Ozawa ◽  
◽  
’Takahide Niimura ◽  
Tomoaki Nakashima ◽  
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Electric Power ◽  
Time Series Data ◽  
Simple Procedure ◽  
Estimation Procedure ◽  
Fuzzy Time Series ◽  
Ar Model ◽  
Electric Power Consumption ◽  
Series Data

In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Tiraditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely stochastic models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy autoregression, and apply it to the analysis of time-series data of electric power consumption. Two models, which are different in complexity, are presented, and the performance of the models are evaluated by vagueness and α-cuts. The proposed fuzzy Auoregression model represents the rich information of uncertainty that the original data contain, and it can be a powerful tool for flexible decision-making with uncertainty. The fuzzy AR model can also be constructed in relatively simple procedure compared with the conventional approaches.

scholarly journals Prediksi Harga Jagung Menggunakan Metode Fuzzy Time Series Dengan Atau Tanpa Menggunakan Markov Chain

2021 ◽  
Vol 4 (1) ◽  
pp. 49-54
Author(s):  
Danung Nur Adli
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data

Penelitian ini dilakukan dengan tujuan memprediksi harga pakan jagung menggunakan salah satu model matematika yang disebut fuzzy time series. Data yang didapatkan yaitu data historis atau rentan waktu dari berbagai literasi seperti hargaweb.id, jagungbisi.com, dan BPS dari tahun 2020-2021, kuarter pertama. Data tersebut nantinya akan dijadikan bahan perhitungan. Data dianalisa menggunakan R Studio. Kemudian algoritma fuzzy time series. hasil penelitian fuzzy time series menghasilkan prediksi harga pada jagung menggunakan time series. Memprediksi harga akan cenderung berubah dari kisaran Rp/ 4.000-4.400,- yang mana tingkat error hanya ada di level 8,23.Logika fuzzy atau time series mampu menyajikan prediksi harga jagung pada tahun 2020-2021 dengan keakuratan dengan tingkat error 8.23% artinya tidak berbeda jauh. Kedepannya banyak model matematika yang bisa digunakan untuk memprediksi dari harga bahan baku atau aspek lainnya pada industri peternakan.

A fuzzy time series forecasting method induced by intuitionistic fuzzy sets

2015 ◽  
Vol 06 (04) ◽  
pp. 1550041 ◽  
Author(s):  
Sanjay Kumar ◽  
Sukhdev Singh Gangwar
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Time Series Data ◽  
Financial Time Series ◽  
Intuitionistic Fuzzy Sets ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Intuitionistic Fuzzy ◽  
Forecasting Method

Intuitionistic fuzzy sets (IFSs) are well established as a tool to handle the hesitation in the decision system. In this research paper, fuzzy sets induced by IFS are used to develop a fuzzy time series forecasting model to incorporate degree of hesitation (nondeterminacy). To improve the forecasting accuracy, induced fuzzy sets are used to establish fuzzy logical relations. To verify the performance of the proposed model, it is implemented on one of the benchmarking time series data. Further, developed forecasting method is also tested and validated by applying it on a financial time series data. In order to show the accuracy in forecasting, the method is compared with other forecasting methods using different error measures.

scholarly journals New hybrid fuzzy time series model: Forecasting the foreign exchange market

2020 ◽  
Vol 66 (3) ◽  
pp. 263
Author(s):  
José Eduardo Medina Reyes ◽  
Salvador Cruz Aké ◽  
Agustín Ignacio Cabrera Llanos
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Foreign Currency ◽  
Time Series Data ◽  
Arima Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Exchange Market

<span class="fontstyle0">This paper develops the comparison of the volatility prediction of the traditional<br />models (ARIMA, EGARCH, and PARCH), with respect to the Hybrid Fuzzy Time<br />Series and Fuzzy ARIMA Model of Tseng’s and Tanaka’s methodology (FTS-Fuzzy<br />ARIMA Tseng and FTS-Fuzzy ARIMA Tanaka). For this purpose, it applies to the<br />time series of the foreign exchange market to forecast the foreign currency exchange rate of Mexican Pesos against American Dollar, the growth rate of the time series data in a daily format from January 2008 to December 2017, to perform the sample test is used January 2018. The main result is that the models based on fuzzy theory generate a better estimate of the volatility of the foreign exchange rate.</span> <br /><br />

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