scholarly journals Implemetasi Model Autoregressive (AR) Dan Autoregressive Conditional Heteroskedasticity (ARCH) Untuk Memprediksi Harga Emas

2018 ◽  
Vol 3 (2) ◽  
pp. 29
Author(s):  
Ni Luh Ketut Dwi Murniati ◽  
Indwiarti Indwiarti ◽  
Aniq Atiqi Rohmawati
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Mean Absolute Error ◽  
Likelihood Estimation ◽  
Absolute Error ◽  
Gold Price ◽  
Conditional Heteroskedasticity ◽  
Mean Square ◽  
Basic Information ◽  
Estimation Of Parameters

Gold is a one of  high selling value items in the market, and it  can be used as an investment item. The price of gold in the market tends to be stable and not undergoing too significant changes which makes gold be a very valuable item. The aim of this research is to predict gold price using AR (1) and ARCH (1) model which are the part of time series methods. The data of gold price is obtained from ANTAM's daily historical website from 2007 - 2017. Here, the basic information about data is given by using descriptive statistic and the estimation of parameters in each model is condacted by using <em>Maximum Likelihood Estimation</em> (MLE). To evaluate the model, <em>Mean Absolute Error</em> (MAE) and <em>Root Mean Square Error</em> (RMSE) are used. In this research, the estimated model of AR (1) and ARCH (1) given as X_t = -0.012X_{t-1}+epsion_t and X_t = epsilon_t sqrt{0.000053+0.011958X^2_{t-1}} respectively. Moreover, the result of MAE and RMSE using AR (1) model are 0.0261 and 0.0342 respectively, meanwhile for ARCH (1) model  are 0.0170 and 0.0251 respectively.

scholarly journals Fractional differentiation and its use in machine learning

2021 ◽  
Author(s):  
Rafał Walasek ◽  
Janusz Gajda
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Prices ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Real Data ◽  
Absolute Error ◽  
Mean Square ◽  
Fractional Differentiation ◽  
Stock Indexes

AbstractThis article covers the implementation of fractional (non-integer order) differentiation on real data of four datasets based on stock prices of main international stock indexes: WIG 20, S&P 500, DAX and Nikkei 225. This concept has been proposed by Lopez de Prado [5] to find the most appropriate balance between zero differentiation and fully differentiated time series. The aim is making time series stationary while keeping its memory and predictive power. In addition, this paper compares fractional and classical differentiation in terms of the effectiveness of artificial neural networks. Root mean square error (RMSE) and mean absolute error (MAE) are employed in this comparison. Our investigations have determined the conclusion that fractional differentiation plays an important role and leads to more accurate predictions in case of ANN.

scholarly journals DETEKSI SEBARAN MUATAN PADATAN TERSUSPENSI DENGAN MODEL EMPIRIS DAN MODEL SEMI-ANALITIK DI PERAIRAN BEKASI

2020 ◽  
Vol 12 (2) ◽  
pp. 341-351
Author(s):  
Pingkan Mayestika Afgatiani ◽  
Maryani Hartuti ◽  
Syarif Budhiman
Keyword(s):  
Time Series ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Mean Square ◽  
Itu Model ◽  
Google Earth Engine

Salah satu parameter dalam kualitas air adalah muatan padatan tersuspensi (MPT). Muatan padatan tersuspensi terdiri dari lumpur, pasir dan jasad renik yang disebabkan pengikisan tanah yang terbawa ke badan air. Penelitian ini bertujuan untuk mendeteksi sedimen tersuspensi di perairan Bekasi. Landsat 8 digunakan untuk analisis padatan tersuspensi dengan platform Google Earth Engine dengan membandingkan antara model empiris dan semi-analitik. Alur studi ini meliputi deliniasi wilayah non air menggunakan data citra surface reflectance, analisis MPT, dan visualisasi. Selanjutnya dilakukan validasi dengan data in situ, pemilihan model dan implementasi time series. Hasil deteksi MPT tertampil dengan tampilan warna yang berbeda sesuai dengan konsentrasinya. Hasil uji validasi dengan data in situ menunjukkan nilai Normalized Mean Absolute Error (NMAE) model semi-analitik lebih mendekati syarat minimum yaitu sebesar 66,8%, berbeda jauh dengan model empiris sebesar 43768%. Nilai Root Mean Square Error (RMSE) pun terlihat bahwa model semi-analitik menghasilkan nilai yang jauh lebih kecil sebesar 51,4 dan model empiris sebesar 58577,2. Hal ini menunjukkan bahwa model semi-analitik memiliki nilai yang lebih baik dalam mendeteksi sebaran MPT. Analisis time series menunjukkan bahwa persebaran MPT tahun 2015 – 2019 di perairan pesisir memiliki sebaran MPT yang sangat tinggi, karena banyaknya tambak dan muara sungai. Oleh karena itu, model semi-analitik lebih direkomendasikan untuk mengestimasi konsentrasi MPT dibandingkan dengan model empiris.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price prediction

2021 ◽  
pp. 1-9
Author(s):  
Rajashree Dash ◽  
Anuradha Routray ◽  
Rasmita Dash ◽  
Rasmita Rautray
Keyword(s):  
Mean Square Error ◽  
Optimization Technique ◽  
Absolute Error ◽  
Time Frame ◽  
Gold Price ◽  
Future Price ◽  
Mean Square ◽  
Learning Speed ◽  
Predictor Model ◽  
Hidden Layer

Predicting future price of Gold has always been an intriguing field of investigation for researchers as well as investors who desire to invest in present and gain profit in the future. Since ancient time, Gold is being arbitrated as a leading asset in monetary business. As the worth of gold changes within confined boundaries, reducing the effect of inflation, so it is a beneficial property favoured by many stakeholders. Hence, there is always an urge of a more authenticate model for forecasting the gold price based upon the changes in it in a previous time frame. This study focuses on designing an efficient predictor model using a Pi-Sigma Neural Network (PSNN) for forecasting future gold. The underlying motivation of using PSNN is its quick learning and easy implementation compared to other neural networks. The fixed unit weights used in between hidden and output layer of PSNN helps it in achieving faster learning speed compared to other similar types of networks. But estimating the unknown weights used in between the input and hidden layer is still a major challenge in its design phase. As final outcome of the network is highly influenced by its weight, so a novel Crow Search based nature inspired optimization algorithm (CSA) is proposed to estimate these adjustable weights of the network. The proposed model is also compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of PSNN. The model is validated over two historical datasets such as Gold/INR and Gold/AED by considering three statistical errors such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Empirical observations clearly show that, the developed CSA-PSNN predictor model is providing better prediction results compared to PSO-PSNN and DE-PSNN model.

scholarly journals Direct Calculation of Thermodynamic Wet-Bulb Temperature as a Function of Pressure and Elevation

2013 ◽  
Vol 30 (8) ◽  
pp. 1757-1765 ◽  
Author(s):  
Sayed-Hossein Sadeghi ◽  
Troy R. Peters ◽  
Douglas R. Cobos ◽  
Henry W. Loescher ◽  
Colin S. Campbell
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Direct Calculation ◽  
Ambient Air ◽  
Absolute Error ◽  
Mean Square ◽  
Air Temperatures ◽  
Order Polynomial ◽  
The Mean ◽  
Very High

Abstract A simple analytical method was developed for directly calculating the thermodynamic wet-bulb temperature from air temperature and the vapor pressure (or relative humidity) at elevations up to 4500 m above MSL was developed. This methodology was based on the fact that the wet-bulb temperature can be closely approximated by a second-order polynomial in both the positive and negative ranges in ambient air temperature. The method in this study builds upon this understanding and provides results for the negative range of air temperatures (−17° to 0°C), so that the maximum observed error in this area is equal to or smaller than −0.17°C. For temperatures ≥0°C, wet-bulb temperature accuracy was ±0.65°C, and larger errors corresponded to very high temperatures (Ta ≥ 39°C) and/or very high or low relative humidities (5% &lt; RH &lt; 10% or RH &gt; 98%). The mean absolute error and the root-mean-square error were 0.15° and 0.2°C, respectively.

scholarly journals Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

2021 ◽  
Author(s):  
FNU SRINIDHI
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Mean Square Error ◽  
Supercritical Co2 ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square ◽  
Relational Analysis ◽  
Grey Relational ◽  
Supercritical Carbon

The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

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