scholarly journals Time-series Modeling for Consumer Price Index Forecasting using Comparison Analysis of AutoRegressive Integrated Moving Average and Artificial Neural Network

2020 ◽  
Author(s):  
Intan Yuniar Purbasari ◽  
Fetty Tri Anggraeny ◽  
Nindy Apsari Ardiningrum
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Time Series Modeling ◽  
Comparison Analysis ◽  
Autoregressive Integrated Moving Average ◽  
Artificial Neural

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

2015 ◽  
Vol 35 (02) ◽  
pp. 241
Author(s):  
Dyah Susilokarti ◽  
Sigit Supadmo Arif ◽  
Sahid Susanto ◽  
Lilik Sutiarso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Fast Fourier Transformation ◽  
Autoregressive Integrated Moving Average ◽  
Precipitation Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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 

scholarly journals Time Series Modelling of Inflation in Botswana Using Monthly Consumer Price Indices

2016 ◽  
Vol 8 (3) ◽  
pp. 15
Author(s):  
Kesaobaka Molebatsi ◽  
Mpho Raboloko
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Price Index ◽  
Moving Average ◽  
Consumer Price Index ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Integrated Moving Average ◽  
Time Series Modelling ◽  
Error Terms ◽  
Autoregressive Conditional Heteroscedasticity

<p>This paper identifies an autoregressive integrated moving average (ARIMA (1,1,1)) model that can be used to model inflation measured by the consumer price index (CPI) for Botswana. The paper proceeds to improve the model by incorporating the generalized autoregressive conditional heteroscedasticity (ARCH/GARCH) model that takes into consideration volatility in the series. Ultimately, CPI is forecast using the two models, ARIMA (1, 1, 1) and ARIMA (1, 1, 1) + GARCH (1, 2) and compared with the actual CPI. Both models perform well in terms of forecasting as their 95 percent confidence intervals cover the actual CPI. Marginal differences that favour the inclusion of the ARCH/GARCH components were observed when testing for normality among error terms. The paper also reveals that volatility for Botswana’s CPI is low as shown by small values of ARCH/GARCH components.</p>

Forecasting inflation in G-7 countries: an application of artificial neural network

foresight ◽  
2015 ◽  
Vol 17 (1) ◽  
pp. 63-73 ◽  
Author(s):  
Sanjeev Gupta ◽  
Sachin Kashyap
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Price Index ◽  
Consumer Price Index ◽  
Suitable Model ◽  
Neural Network Models ◽  
Policy Makers ◽  
Content Type ◽  
Artificial Neural ◽  
Forecasting Inflation

Purpose – The paper aims to evaluate different artificial neural network models and to suggest a suitable model for forecasting inflation in G-7 countries. Design/methodology/approach – The study applies different combinations of neural networks with hyperbolic tangent function using backpropagation learning with the steepest gradient descent technique to monthly data on Consumer Price Index (a measure of inflation) of the USA, the UK, France, Germany, Italy, Japan and Canada. Findings – Predictions of inflation based on the Consumer Price Index for all the seven countries divulged that it is expected that the rate of inflation will decline marginally in the near future. Practical implications – The results proposed in this study will be a benchmark for policy-makers, economists and practitioners to forecast inflation and design policies accordingly. Originality/value – The paper’s findings provide strong evidence for policy-makers that while constructing models for forecasting inflation, the suggested models can be used to track the future rates of inflation and, further, they can apply that model in framing policies.

scholarly journals Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study

2021 ◽  
Author(s):  
Nabeel H. Al-Saati ◽  
Isam I. Omran ◽  
Alaa Ali Salman ◽  
Zainab Al-Saati ◽  
Khalid S. Hashim
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Moving Average ◽  
Stationary Time Series ◽  
Coefficient Of Determination ◽  
Neural Network Models ◽  
Monthly Streamflow ◽  
Artificial Neural

Abstract Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination R2 = 0.801), while the (NAR) model gave (RMSE = 93.4) and R2 = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity.

scholarly journals Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural Networks

2018 ◽  
Vol 7 (1) ◽  
pp. 115
Author(s):  
Jayrani Cheeneebash ◽  
Ashvin Harradon ◽  
Ashvin Gopaul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Geographical Location ◽  
Absolute Difference ◽  
Autoregressive Integrated Moving Average ◽  
Percentage Difference ◽  
Absolute Percentage Difference ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper, two forecasting methods namely, the autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) are studied to forecast the amount of rainfall in Mauritius. Indeed due to the geographical location of Mauritius, the rainfall pattern is deeply affected by the season prevailing whereby the period of summer receives a relatively high amount of rainfall when compared to winter. As such, forecasting rainfall can help the local authorities to manage the distribution of water in the country especially during droughts. The results obtained from both methods are compared in terms of their mean square error, mean absolute difference and mean absolute percentage difference. It is then seen that artificial neural network is a much better model as it is more accurate. This is due to its nonlinearity characteristic and ability to learn and train itself.

Sign in / Sign up

Export Citation Format

Share Document