scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

Using AutoRegressive Integrated Moving Average and Gaussian Processes with LSTM neural networks to predict discrete geomagnetic signals

2020 ◽  
Author(s):  
Laurentiu Asimopolos ◽  
Alexandru Stanciu ◽  
Natalia-Silvia Asimopolos ◽  
Bogdan Balea ◽  
Andreea Dinu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Frequency Information ◽  
Probabilistic Forecasts ◽  
Geomagnetic Data ◽  
Gp Model

<p>In this paper, we present the results obtained for the geomagnetic data acquired at the Surlari Observatory, located about 30 Km North of Bucharest - Romania. The observatory database contains records from the last seven solar cycles, with different sampling rates.</p><p>We used AR, MA, ARMA and ARIMA (AutoRegressive Integrated Moving Average) type models for time series forecasting and phenomenological extrapolation. ARIMA model is a generalization of an autoregressive moving average (ARMA) model, fitted to time series data to predict future points in the series</p><p>We made spectral analysis using Fourier Transform, that gives us a relevant picture of the frequency spectrum of the signal component, but without locating it in time, while the wavelet analysis provides us with information regarding the time of occurrence of these frequencies. </p><p>Wavelet allows local analysis of magnetic field components through variable frequency windows. Windows with longer time intervals allow us to extract low-frequency information, medium-sized intervals of different sizes lead to medium-frequency information extraction, and very narrow windows highlight the high-frequencies or details of the analysed signals.</p><p>We extend the study of geomagnetic data analysis and predictive modelling by implementing a Long Short-Term Memory (LSTM) recurrent neural network that is capable of modelling long-term dependencies and is suitable for time series forecasting. This method includes a Gaussian process (GP) model in order to obtain probabilistic forecasts based on the LSTM outputs. </p><p>The evaluation of the proposed hybrid model is conducted using the Receiver Operating Characteristic (ROC) Curve that provides a probabilistic forecast of geomagnetic storm events. </p><p>In addition, reliability diagrams are provided in order to support the analysis of the probabilistic forecasting models.</p><p>The implementation of the solution for predicting certain geomagnetic parameters is implemented in the MATLAB language, using the Toolbox Deep Learning Toolbox, which provides a framework for the design and implementation of deep learning models.</p><p>Also, in addition to using the MATLAB environment, the solution can be accessed, modified, or improved in the Jupyter Notebook computing environment.</p>

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

scholarly journals Self-Identification ResNet-ARIMA Forecasting Model

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Model Identification ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Partial Autocorrelation Function

The challenging endeavor of a time series forecast model is to predict the future time series data accurately. Traditionally, the fundamental forecasting model in time series analysis is the autoregressive integrated moving average model or the ARIMA model requiring a model identification of a three-component vector which are the autoregressive order, the differencing order, and the moving average order before fitting coefficients of the model via the Box-Jenkins method. A model identification is analyzed via the sample autocorrelation function and the sample partial autocorrelation function which are effective tools for identifying the ARMA order but it is quite difficult for analysts. Even though a likelihood based-method is presented to automate this process by varying the ARIMA order and choosing the best one with the smallest criteria, such as Akaike information criterion. Nevertheless the obtained ARIMA model may not pass the residual diagnostic test. This paper presents the residual neural network model, called the self-identification ResNet-ARIMA order model to automatically learn the ARIMA order from known ARIMA time series data via sample autocorrelation function, the sample partial autocorrelation function and differencing time series images. In this work, the training time series data are randomly simulated and checked for stationary and invertibility properties before they are used. The result order from the model is used to generate and fit the ARIMA model by the Box-Jenkins method for predicting future values. The whole process of the forecasting time series algorithm is called the self-identification ResNet-ARIMA algorithm. The performance of the residual neural network model is evaluated by Precision, Recall and F1-score and is compared with the likelihood basedmethod and ResNET50. In addition, the performance of the forecasting time series algorithm is applied to the real world datasets to ensure the reliability by mean absolute percentage error, symmetric mean absolute percentage error, mean absolute error and root mean square error and this algorithm is confirmed with the residual diagnostic checks by the Ljung-Box test. From the experimental results, the new methodologies of this research outperforms other models in terms of identifying the order and predicting the future values.

scholarly journals Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type

2019 ◽  
Vol 23 (5) ◽  
pp. 324-329
Author(s):  
Roberto L. da S. Carvalho ◽  
Angel R. S. Delgado
Keyword(s):  
Time Series ◽  
Reference Evapotranspiration ◽  
Moving Average ◽  
Group Method ◽  
Data Handling ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Irrigation Methods ◽  
Artificial Neural Network Ann ◽  
Climatic Time Series

ABSTRACT Reference evapotranspiration is a climatological variable of great importance for water use dimensioning in irrigation methods. In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology. Data from the National Institute of Meteorology - INMET, obtained at the Automatic Weather Station of Ariquemes, from January 2011 to January 2014, were used. Data analysis was performed using software R version 3.3.1 through the GMDH-type ANN package. Modeling by GMDH-type ANN led to results similar to the results of the SARIMA model, thus constituting an option to predict climatic time series. GMDH-type models with larger numbers of inputs and layers presented lowest mean square error.

scholarly journals Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yi-Hui Pang ◽  
Hong-Bo Wang ◽  
Jian-Jian Zhao ◽  
De-Yong Shang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Sliding Window ◽  
Series Data ◽  
Average Model ◽  
Hydraulic Support ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

Using a GMDH-type neural network and ARIMA model to forecasting GDP in Algeria during the period of 1990-2019

2020 ◽  
Vol 3 (4) ◽  
pp. 37-47
Author(s):  
Abdelkader Sahed
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Group Method ◽  
Data Handling ◽  
Performance Indices ◽  
Promising Technique ◽  
Using Data ◽  
Artificial Neural Network Ann

Forecasting is a method to predict the future using data and the last information as a tool assists in planning to be effective. GMDH-Type (Group Method of Data Handling) artificial neural network (ANN) and Box-Jenkins method are among the know methods for time series forecasting of mathematical modeling. in the present study  GMDH-type neural network and ARIMA method has been used to forecasted GDP in Algeria during the period 1990 to2019 (Time series of quarterly observations on Gross Domestic Product (GDP) is used). Root mean square error (RMSE) was used as performance indices to test the accuracy of the forecast. The empirical results for both models showed that the GMDH model is a powerful tool in forecasting GDP and it provides a promising technique in time series forecasting methods.

scholarly journals Comparing of ARIMA and RBFNN for short-term forecasting

2015 ◽  
Vol 1 (1) ◽  
pp. 15 ◽  
Author(s):  
Haviluddin Haviluddin ◽  
Ahmad Jawahir
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Short Term Forecasting ◽  
Simulated Time ◽  
Modelling And Forecasting

Based on a combination of an autoregressive integrated moving average (ARIMA) and a radial basis function neural network (RBFNN), a time-series forecasting model is proposed. The proposed model has examined using simulated time series data of tourist arrival to Indonesia recently published by BPS Indonesia. The results demonstrate that the proposed RBFNN is more competent in modelling and forecasting time series than an ARIMA model which is indicated by mean square error (MSE) values. Based on the results obtained, RBFNN model is recommended as an alternative to existing method because it has a simple structure and can produce reasonable forecasts.

scholarly journals Prediction Of Tiger Shrimp Supply Using Time Series Anlysis Method Case Study CV.Surya Perdana Benur

2021 ◽  
Vol 3 (1) ◽  
pp. 27-32
Author(s):  
Yoesril Ihza Mahendra ◽  
Natalia Damastuti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Economic Value ◽  
Series Data ◽  
Model Parameters ◽  
Marine Animal ◽  
Autoregressive Integrated Moving Average ◽  
Tiger Shrimp

Prediction of demand for tiger shrimp buyers using data from the company CV. Surya Perdana Benur. The process is carried out with the models in the Autoregressive Integrated Moving Average method. Tiger shrimp is a marine animal that is now widely cultivated by big company in Indonesia. Tiger shrimp has important economic value, so its existence must be maintained as part of Indonesian germplasm. The problem now faced by many tiger shrimp companies is the inadequate availability of goods for consumers. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. This time series data method is useful for predicting the availability of goods for consumers who want to buy goods at the company CV. Surya Perdana Benur. Autoregressive (AR), MovingAverage (MA), and Autoregressive Integrated Moving Average (ARIMA) model and will be evaluated through Mean Absolute Percent Error (MAPE). The initial process that will be carried out after the data is processed is model identification, estimation of model parameters, residual inspection, using forecasting models if the model has been fulfilled will be evaluated using MAPE until the results come out 14875.593875 to be able to predict the next buyer demand.

scholarly journals Trends in marine fish production in Tamil Nadu using regression and autoregressive integrated moving average (ARIMA) model

2017 ◽  
Vol 9 (2) ◽  
pp. 653-657 ◽  
Author(s):  
A. Anuja ◽  
V. K. Yadav ◽  
V. S. Bharti ◽  
N. R. Kumar
Keyword(s):  
Time Series ◽  
Marine Fish ◽  
Tamil Nadu ◽  
Moving Average ◽  
Arima Model ◽  
Eastern Coast ◽  
Series Data ◽  
Fish Production ◽  
Autoregressive Integrated Moving Average ◽  
Significant Difference

Tamil Nadu is situated in the south eastern coast of the Indian peninsula with a coastal line of 1076 km (13% of the country’s coast line), 0.19 million sq.km of EEZ (9.4 % of total national EEZ) and a continental shelf of about 41,412 sq. km. This is one of the country’s leading state in marine fish production and ranks third in marine fish production. In Tamil Nadu, Ramanathapuram district is a leading maritime district followed by Nagapattinam and Thoothukudi. The objective of this study was to investigate the trends in marine fish production in Tamil Nadu. Yearly fish production data for the period of 1988-1989 to 2012-2013 were analyzed using time-series method called Autoregressive Integrated Moving Average (ARIMA) model and Regression analysis (curve estimation). In our study, the developed best ARIMA model for Tamil Nadu marine fish production was found to be ARIMA (1, 1, 1) which have the minimum BIC (Bayesian Information Criterion). ARIMA model had got a slightly higher forecasting accuracy rate for forecasting marine fish production of Tamil Nadu than Regression trend analysis. The independent sample test showed there was no significant difference between the two models. The limitations of ARIMA model include its requirement of a long time series data for better forecast. It is basically linear model assuming that data are stationary and have a limited ability to capture non-stationarities and nonlinearities in series data. Both the models indicated that Tamil Nadu marine fish production has plateaued and fishermen should be encouraged to adopt sustainable fishing practices.

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