Forecasting Agricultural Commodity Prices using Hybrid Neural Networks

2007 ◽  
pp. 63-74 ◽  
Author(s):  
Tamer Shahwan ◽  
Martin Odening
Keyword(s):  
Neural Networks ◽  
Commodity Prices ◽  
Agricultural Commodity ◽  
Hybrid Neural Networks

scholarly journals Memprediksi Harga Komoditas Cabe Menggunakan Metode Backpropagation di Wilayah Kota Payakumbuh

KOMTEKINFO ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 33-48
Author(s):  
Allans Prima Aulia ◽  
Yuhandri ◽  
Fhajri Arye Gemilang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Commodity Prices ◽  
Test Results ◽  
Agricultural Commodity ◽  
Data Format ◽  
Squared Error ◽  
Backpropagation Method

Chili is one of the spices needed by the majority of Indonesian people. These high needs have an impact on the price of this agricultural commodity which has become very fluctuated. This study, uses the backpropagation method to predict chilli prices in Payakumbuh City, with data sourced from the Badan Pusat Statistik Kota Payakumbuh. The data format are weekly chilli price data for the period 2014 to 2019. Data variables are arranged into time series forms with 4 input values from each week per year, and 1 target value. From the test results obtained the MSE value (Mean Squared Error) of 0.00118 with prediction accuracy of 98.56%. The results of this study can prove that Artificial Neural Networks using the backpropagation method can predict commodity prices for chilli in Payakumbuh City with a good level of accuracy, so that it can be used for the following year.

Estimation of economic specialization of Ukrainian regions in the context of global trends

2019 ◽  
pp. 24-31
Author(s):  
Svitlana Ishchuk ◽  
Lyubomyr Sozanskyy
Keyword(s):  
National Economy ◽  
Economic Activity ◽  
Commodity Prices ◽  
Value Added ◽  
High Tech ◽  
Agricultural Commodity ◽  
Macroeconomic Factors ◽  
Gross Value Added ◽  
Its Regions ◽  
Economic Specialization

The scale and deep heterogeneity of the national economy of Ukraine in the regional context make the relevance of scientific research in this thematic area. The purpose of the article is to determine the economic specialization of the regions of Ukraine by key economic activities contributing to the formation of gross value added, as well as outlining the potential risks to the national economy, taking into account the situation on world commodity markets. The results of the research showed that one of the consequences of the unstable dynamics of industrial production in Ukraine under the influence of geopolitical and macroeconomic factors is the reduction of industrial specialization of the economy of a number of Ukrainian regions. Thus, in 2017 the manufacturing was the leading economic activity (with the highest share in the gross value added created) in 11 regions, compared to 15 in 2012. So Poltava, Donetsk, Dnipropetrovsk and Zaporizhzhya regions are considered to be “highly industrial”. At the same time, the agrarian specialization of the economy of Ukraine and its regions deepened – in 2017 agriculture became the leading type of economic activity in 11 regions (compared to 7 in 2012). The most “agrarian” in Ukraine (with a share of agriculture in gross value added over 30%) in 2017 became the Kherson, Kirovohrad and Khmelnytsky regions. Increasing the level of “agrarianization” of the national economy in the context of volatility of agricultural commodity prices on the world markets poses significant risks for the socio-economic development of Ukraine and its regions. These risks are exacerbated by the high amplitude of fluctuations in the volume and structure of domestic agricultural products and the low degree of processing of raw materials. To improve the structure of domestic commodity exports (in the direction of increasing its share of products with a higher degree of processing) and to deepen its diversification, a number of measures should be carried out aimed at stimulating export activity of enterprises (industrial and agro-industrial), carrying out technical and technological re-equipment of industrial and export production bases, creation of new high-tech industries on the basis of the implementation of powerful innovation and investment projects.

Prediction of Future Market Price for Agricultural Commodities

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Sagar Pathane ◽  
Uttam Patil ◽  
Nandini Sidnal
Keyword(s):  
Mobile Application ◽  
Market Price ◽  
Commodity Prices ◽  
Future Price ◽  
Future Market ◽  
Graphical Interface ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Web Based ◽  
Agriculture Products

The agricultural commodity prices have a volatile nature which may increase or decrease inconsistently causing an adverse effect on the economy. The work carried out here for predicting prices of agricultural commodities is useful for the farmers because of which they can sow appropriate crop depending on its future price. Agriculture products have seasonal rates, these rates are spread over the entire year. If these rates are known/alerted to the farmers in advance, then it will be promising on ROI (Return on Investments). It requires that the rates of the agricultural products updated into the dataset of each state and each crop, in this application five crops are considered. The predictions are done based on neural networks Neuroph framework in java platform and also the previous years data. The results are produced on mobile application using android. Web based interface is also provided for displaying processed commodity rates in graphical interface. Agricultural experts can follow these graphs and predict market rates which can be informed to the farmers. The results will be provided based on the location of the users of this application.

CNN-BiLSTM Hybrid Neural Networks with Attention Mechanism for Well Log Prediction

2021 ◽  
pp. 108838
Author(s):  
Liqun Shan ◽  
Yanchang Liu ◽  
Min Tang ◽  
Ming Yang ◽  
Xueyuan Bai
Keyword(s):  
Neural Networks ◽  
Attention Mechanism ◽  
Well Log ◽  
Hybrid Neural Networks

scholarly journals Roadmap on material-function mapping for photonic-electronic hybrid neural networks

APL Materials ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 100903 ◽  
Author(s):  
Mario Miscuglio ◽  
Gina C. Adam ◽  
Duygu Kuzum ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Material Function ◽  
Hybrid Neural Networks ◽  
Function Mapping

Detection of Heart Arrhythmia Using Hybrid Neural Networks

2020 ◽  
Author(s):  
Shreeven Kommireddy ◽  
Piyush Raj Pandey ◽  
Raghu Kishore NeeliSetti
Keyword(s):  
Neural Networks ◽  
Heart Arrhythmia ◽  
Hybrid Neural Networks

scholarly journals Spatio-temporal hybrid neural networks reduce erroneous human “judgement calls” in the diagnosis of Takotsubo syndrome

2021 ◽  
Vol 40 ◽  
pp. 101115
Author(s):  
Fahim Zaman ◽  
Rakesh Ponnapureddy ◽  
Yi Grace Wang ◽  
Amanda Chang ◽  
Linda M Cadaret ◽  
...  
Keyword(s):  
Neural Networks ◽  
Takotsubo Syndrome ◽  
Spatio Temporal ◽  
Hybrid Neural Networks
Sign in / Sign up

Export Citation Format

Share Document