Artificial Neural Network for Markov Chaining of Rainfall Over India

Time Series Data ◽

Mean Squared Error ◽

Multiple Linear Regression Model ◽

Monthly Rainfall ◽

Series Data ◽

Rainfall Forecasting ◽

Artificial Neural ◽

Rainfall forecasting plays a significant role in water management for agriculture in a country like India where the economy depends heavily upon agriculture. In this paper, a feed forward artificial neural network (ANN) and a multiple linear regression model has been utilized for lagged time series data of monthly rainfall. The data for 23 years from 1990 to 2012 over Indian region has been used in this study. Convincing values of root mean squared error between actual monthly rainfall and that predicted by ANN has been found. It has been found that during monsoon months, rainfall of every n+3rd month can be predicted using last three months' (n, n+1, n+2) rainfall data with an excellent correlation coefficient that is more than 0.9 between actual and predicted rainfall. The probabilities of dry seasonal month, wet seasonal month for monsoon and non-monsoon months have been found.

STUDI KOMPARASI PREDIKSI CURAH HUJAN METODE FAST FOURIER TRANSFORMATION (FFT), AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN ARTIFICIAL NEURAL NETWORK (ANN)

Jurnal Agritech ◽

10.22146/agritech.9412 ◽

2015 ◽

Vol 35 (02) ◽

pp. 241

Author(s):

Dyah Susilokarti ◽

Sigit Supadmo Arif ◽

Sahid Susanto ◽

Lilik Sutiarso

Keyword(s):

Neural Network ◽

Time Series Data ◽

Moving Average ◽

Series Data ◽

Fast Fourier Transformation ◽

Autoregressive Integrated Moving Average ◽

Precipitation Prediction ◽

Artificial Neural ◽

Optimum climate condition and water availability are essential to support strategic venue and time for plants to grow and produce. Precipitation prediction is needed to determine how much precipitation will provide water for plants on each stage of growth. Nowadays, the high variability of precipitation calls for a prediction model that will accurately foreseethe precipitation condition in the future. The prediction conducted is based on time-series data analysis. The research aims to comparethe effectiveness of three precipitation prediction methods, which are Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). Their respective performances are determined by their Mean Square Error (MSE) values. Methods with highest correlation values and lowest MSE shows the best performance. The MSE result for FFT is 14,92; ARIMA is 17,49; and ANN is 0,07. This research concluded that Artificial Neural Network (ANN) method showed best performance compare to the other two because it had produced a prediction with the lowest MSE value.Keywords: Precipitation prediction, Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average ABSTRAKKondisi iklim dan ketersediaan air yang optimal bagi pertumbuhan dan perkembangan tanaman sangat diperlukan dalam upaya mendukung strategi budidaya tanaman sesuai ruang dan waktu. Prediksi curah hujan sangat diperlukan untuk untuk mengetahui sejauh mana curah hujan dapat memenuhi kebutuhan air pada setiap tahap pertumbuhantanaman. Variabilitas curah hujan yang tinggi saat ini, membutuhkan pemodelan yang dapat memprediksi secara akurat bagaimana kondisi curah hujan dimasa yang akan datang. Prediksi yang dilakukan adalah prediksi berdasarkan urutan waktu (). Tujuan dari penelitian ini adalah untuk membandingkan akurasi prediksi curah hujan antara metode (FFT), (ARIMA) dan (ANN). Kinerja ketiga metode yang digunakan dilihat dari nilai (MSE). Metode dengan nilai korelasi tertinggi dan nilai MSE terkecil menunjukkan kinerja terbaik. Hasil penelitan untuk FFT diperoleh nilai MSE = 14,92, ARIMA = 17,49 sedangkan ANN = 0,07. Ini menunjukkan bahwa metode (ANN) menunjukkan kinerja yang paling baik diantara dua metode lainnya karena menghasilkan prediksi yangmempunyai nilai MSE terkecil.Kata kunci: Prediksi curah hujan,FFT, ARIMA dan ANN

Aplikasi Artificial Neural Network (ANN) dalamPeramalan Time Series Data: StudiKasus Lama SinarMatahari

IJECA (International Journal of Education and Curriculum Application) ◽

10.31764/ijeca.v0i0.2004 ◽

2018 ◽

pp. 207

Author(s):

Sulistyarini Sulistyarini

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Data ◽

Series Data ◽

Marriage Ceremony ◽

Library Research ◽

Wedding Ceremony ◽

Artificial Neural ◽

This paper discusses wedding ceremony in Central Lombok village of Plambik, which is potential to be a cultural attraction that supports the development of tourism. Marriage ceremony in Plambik has a number of stages, which are not necessarily similar to those customly practiced by other groups of Sasak people. in order to hold a wedding ceremony. This paper aimed to explore merariq tradition which is uniquely held by Sasak community in Plambik. Data of this research were collected through library research and interviews with Plambik natives. The data were then analyzed by comparing the documentary notes with the actual practices of merariq by Plambik villagers. The finding indicated unique features of merariq stages in Plambik.

Multifraktalitas dan Studi Komparatif Prediksi Indeks dengan Metode Arima dan Artificial Neural Network (ANN)

The Winners ◽

10.21512/tw.v9i2.720 ◽

2008 ◽

Vol 9 (2) ◽

pp. 112

Author(s):

Harjum Muharam ◽

Muhammad Panji

Keyword(s):

Neural Network ◽

Time Series Data ◽

Moving Average ◽

Back Propagation ◽

Series Data ◽

Future Value ◽

Auto Regressive ◽

Artificial Neural ◽

This paper discusses technical analysis widely used by investors. There are many methods that exist and used by investor to predict the future value of a stock. In this paper we start from finding the value of Hurst (H) exponent of LQ 45 Index to know the form of the Index. From H value, we could determinate that the time series data is purely random, or ergodic and ant persistent, or persistent to a certain trend. Two prediction tools were chosen, ARIMA (Auto Regressive Integrated Moving Average) which is the de facto standard for univariate prediction model in econometrics and Artificial Neural Network (ANN) Back Propagation. Data left from ARIMA is used as an input for both methods. We compared prediction error from each method to determine which method is better. The result shows that LQ45 Index is persistent to a certain trend therefore predictable and for outputted sample data ARIMA outperforms ANN.

Dynamic prediction of Indian stock market: an artificial neural network approach

International Journal of Ethics and Systems ◽

10.1108/ijoes-11-2020-0184 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Himanshu Goel ◽

Narinder Pal Singh

Keyword(s):

Neural Network ◽

Stock Market ◽

Institutional Investors ◽

Time Series Data ◽

Gradient Algorithm ◽

Series Data ◽

Content Type ◽

Artificial Neural ◽

Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models

Dhaka University Journal of Science ◽

10.3329/dujs.v68i2.54612 ◽

2020 ◽

Vol 68 (2) ◽

pp. 143-147

Author(s):

Abira Sultana ◽

Murshida Khanam

Keyword(s):

Neural Network ◽

Time Series Data ◽

Arima Model ◽

Rice Production ◽

Series Data ◽

Ann Model ◽

Neural Network Models ◽

Artificial Neural ◽

Estimated Error

Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

PENERAPAN ARTIFICIAL NEURAL NETWORK DENGAN OPTIMASI MODIFIED ARTIFICIAL BEE COLONY UNTUK MERAMALKAN HARGA BITCOIN TERHADAP RUPIAH

Jurnal Gaussian ◽

10.14710/j.gauss.v9i2.27815 ◽

2020 ◽

Vol 9 (2) ◽

pp. 135-142

Author(s):

Di Mokhammad Hakim Ilmawan ◽

Budi Warsito ◽

Sugito Sugito

Keyword(s):

Neural Network ◽

Artificial Bee Colony ◽

Time Series Data ◽

Learning Algorithm ◽

Series Data ◽

Future Period ◽

Bee Colony ◽

Artificial Neural ◽

Digital Assets

Bitcoin is one of digital assets that can be used to make a profit. One of the ways to use Bitcoin profitly is to trade Bitcoin. At trade activities, decisions making whether to buy or not are very crucial. If we can predict the price of Bitcoin in the future period, we can make a decisions whether to buy Bitcoin or not. Artificial Neural Network can be used to predict Bitcoin price data which is time series data. There are many learning algorithm in Artificial Neural Network, Modified Artificial Bee Colony is one of optimization algorithm that used to solve the optimal weight of Artificial Neural Network. In this study, the Bitcoin exchage rate against Rupiah starting September 1, 2017 to January 4, 2019 are used. Based on the training results obtained that MAPE value is 3,12% and the testing results obtained that MAPE value is 2,02%. This represent that the prediction results from Artificial Neural Network optimized by Modified Artificial Bee Colony algorithm are quite accurate because of small MAPE value.

Feature extraction for time-series data: An artificial neural network evolutionary training model for the management of mountainous watersheds

Neurocomputing ◽

10.1016/j.neucom.2008.08.024 ◽

2009 ◽

Vol 73 (1-3) ◽

pp. 49-59 ◽

Cited By ~ 12

Author(s):

Thomas J. Glezakos ◽

Theodore A. Tsiligiridis ◽

Lazaros S. Iliadis ◽

Constantine P. Yialouris ◽

Fotis P. Maris ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Feature Extraction ◽

Time Series Data ◽

Training Model ◽

Series Data ◽

Artificial Neural ◽

Mountainous Watersheds

APLIKASI PERAMALAN JUMLAH KELAHIRAN DENGAN METODE JARINGAN SYARAF TIRUAN

The Indonesian Journal of Public Health ◽

10.20473/ijph.v12i2.2017.213-223 ◽

2018 ◽

Vol 12 (2) ◽

pp. 213

Author(s):

Safira Amudya Nurdela

Keyword(s):

Neural Network ◽

Time Series Data ◽

Secondary Data ◽

Control Program ◽

Series Data ◽

Neural Network Method ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural

The forecast is a statistic analysis to predict what it will happen in the future using the data and information from the past. This research aimed to apply Artificial Neural Network method for estimate the f ertility rate in Surabaya. The study was descriptive which using secondary data providing from Dinas Kesehatan Kota Surabaya. The study used time series data by recapitulation of fertility rate monthly from 2012-2016. The data analysis used R Program. The result showed the best estimator model for Artificial Neural Network method was 1-3-1 architecture with preprocessing normalized. RMS value of Artificial Neural Network method was 338.1551. The conclusion of this research was the Artificial Neural Network method for estimate the f ertility rate in Surabaya could be used for planning birth control program especially Badan Kependudukan dan Keluarga Berencana Nasional.

Prediction of rubber vulcanization using an artificial neural network

Hemijska industrija ◽

10.2298/hemind210511026l ◽

2021 ◽

Vol 75 (5) ◽

pp. 277-283

Author(s):

Jelena Lubura ◽

Predrag Kojic ◽

Jelena Pavlicevic ◽

Bojana Ikonic ◽

Radovan Omorjan ◽

...

Keyword(s):

Neural Network ◽

Large Scale ◽

Mean Squared Error ◽

Percentage Error ◽

Dense Layer ◽

Ann Model ◽

Different Temperatures ◽

Artificial Neural ◽

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.