PENDUGAAN PARAMETER MODEl DISTRIBUTED LAG POLA POLINOMIAL MENGGUNAKAN METODE ALMON

The distributed lag model is a regression model that describes the relationship between the dependent variable of a given period and the independent variables of a certain or previous periods. The model can be used to determine the impact of the independent variable to dependent variables over time and forecast time series data for the next periods. There are two forms of distributed lag model that have been widely proposed in the estimation of distributed lag regression model. The first form is proposed by Koyck and the second form by Almon. This paper aims to apply the Almon model to examine the effect of the ratio of BOPO (Operating Cost and Operating Income) to the ROA (Return on Asset) of a government bank based on quarterly data, to estimate its parameters, to examine the feasibility of the model, and to predict the next quarter. Results shows that distributed lag model is = 10.110 - 0.078 + 0.015 + 0.026 – 0.045 with Yt is ROA, and Xt is the ratio BOPO on the 1st quarter until the previous 3 quarters. The model is quite good according to the determination coefficient that is 0.75, no autocorrelation in the model, t test and F test are also significant. Based on the model, the value of ROA ratio next quarter predicted 4.63%. The decrease in profitability ROA ratio is due to an increase in interest expense while interest income can not compensate

Determining the likely localization of methane sources using forecast time series and satellite data

The paper is devoted to the topical problem of determining the sources of methane from observational data. An algorithm based on the statistical optimization method used to estimate a time constant parameter is considered. To implement the algorithm, a variant of ensemble smoothing is used, which is an optimal estimate of the desired parameter based on observational data and forecast for a given time interval. This paper presents the implementation of the algorithm for real observational and forecast data, the results of a three-dimensional transport and diffusion model are taken as a mathematical model, and satellite measurement data are used as observational data. Methane fluxes are estimated in subdomains of the Earth’s surface for specified time intervals. The paper contains a mathematical formulation of the problem, a scheme for its numerical implementation. The results of numerical experiments with model and real data are presented.

Flicker in the Networks with Distributed Generation Plants and Its Removal Using Group Prognostic Controller

The use of distributed generation (DG) plants in electrical energy system (EES) produces unambiguous effect on power quality. The presence of DG plants allows to reduce losses associated with power transmission and maintain the required voltage levels. In this case, the presence of DG can cause voltage fluctuation, leading to the appearance of flicker, which is understood as a feeling of instability of visual perception. Similar processes can occur at sharp disturbances close proximity to the DG. The situation can be aggravated by improperly configured DG generators controllers. Therefore, it is necessary to conduct an accurate assessment of the DG plants impact on the power grid, which is a rather time consuming task. The article presents results of the EES working modes simulation with a DG plant implemented on the basis of synchronous turbine generators. The results obtained indicated that during temporary connection of heavy load in the connection unit of DG plant and the use of non-concordantly tuned controllers, there are fluctuations in rotor speed and voltage of generators, the analysis of which indicates the presence of flicker. The same effect can obtained a sudden change in the forecast time for individual controllers of turbine generators speed. Flicker can be removed by applying group control of generators speed controllers.

Interest rate forecasts in Latin America

PurposeThis paper aims to assess the quality of interest rate forecasts for the money markets in Argentina, Brazil, Chile, Mexico and Venezuela for the period between 2001 and 2019. Future interest rate trends are of key significance for many business-related decisions. Thus, reliable interest rate forecasts are essential, for example, for banks that make profits by carrying out maturity transformations.Design/methodology/approachThe data that we analyze were collected by Consensus Economics through a monthly survey with over 120 renowned economists and were published between 2001 and 2019 in the journal Latin American Consensus Forecasts. The authors use the Diebold-Mariano test, the sign accuracy test, the TOTA coefficient and the unbiasedness test to determine the precision and biasedness of the forecasts.FindingsThe research reveals that the forecasting work carried out in Brazil, Chile and Mexico is remarkably successful. The quality of forecasts from Argentina and Venezuela, on the other hand, is significantly poorer.Originality/valueOver 50 studies have already been published with regard to the accuracy of interest rate forecasts, emphasizing the importance of the topic. However, interest rate forecasts for Latin American money markets have hardly been considered thus far. The paper closes this research gap. Overall, the analyzed database amounts to a total of 209 forecast time series with 28,451 individual interest rate forecasts. This study is thus far more comprehensive than all previous studies.

Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility

This study examines the volatility of nine leading cryptocurrencies by market capitalization—Bitcoin, XRP, Ethereum, Bitcoin Cash, Stellar, Litecoin, TRON, Cardano, and IOTA-by using a Bayesian Stochastic Volatility (SV) model and several GARCH models. We find that when we deal with extremely volatile financial data, such as cryptocurrencies, the SV model performs better than the GARCH family models. Moreover, the forecasting errors of the SV model, compared with the GARCH models, tend to be more accurate as forecast time horizons are longer. This deepens our insight into volatility forecast models in the complex market of cryptocurrencies.

Impact of the observed SST frequency in the model initialization on the BSISO prediction

The impact of the observed sea surface temperature (SST) frequency in the model initialization on the prediction of the boreal summer intraseasonal oscillation (BSISO) over the Western North Pacific (WNP) is investigated using the Beijing Climate Center Climate System Model. Three sets of hindcast experiments initialized by the observed monthly, weekly and daily SST data (referred to as the Exp_MSST, Exp_WSST and Exp_DSST, respectively) are conducted with 3-month integration starting from the 1st, 11th, and 21st day of each month in June–August during 2000–2014, respectively. The results show that the useful prediction skill of BSISO index reaches out to about 10 days in the Exp_MSST, and further increases by 1–2 days in the Exp_WSST and Exp_DSST. The skill differences among various hindcast experiments are especially apparent during the forecast time of 6–20 days. Focusing on the strong BSISO cases in this period, the BSISO activity and its related moist static energy (MSE) characteristics over the WNP are further diagnosed. It is found that from the Exp_MSST to the Exp_WSST and Exp_DSST, the enhanced BSISO prediction skill is associated with the more realistic variations of intraseasonal MSE and its tendency. Among the various budget terms that dominate the MSE tendency, the surface latent heat flux and MSE advection are evidently improved, with reduction of mean biases by more than 21% and 10%, respectively. Therefore, the better reproduced MSE variation may contribute to the more skillful BSISO forecast through improving the surface evaporation as well as atmospheric convergence and divergence that related to the BSISO activity. Our findings suggest the necessity of increasing the observed SST frequency (i.e., from monthly to weekly or daily) in the initialization process of coupled models to enhance the actual BSISO predictability, since some current subseasonal forecast operations and researches still use relatively low-frequency SST observations for the model initialization.

Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning

Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.

Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests

The existence of multiple wave forecasts leads to the question of which one should be used in practical ocean engineering applications. Ensemble forecasts have emerged as an important complement to deterministic forecasts, with better performances at mid-to-long ranges; however, they add another option to the variety of wave predictions that are available nowadays. This study developed random forest (RF) postprocessing models to identify the best wave forecast between two National Centers for Environmental Protection (NCEP) products (deterministic and ensemble). The supervised learning classifier was trained using National Data Buoy Center (NDBC) buoy data and the RF model accuracies were analyzed as a function of the forecast time. A careful feature selection was performed by evaluating the impact of the wind and wave variables (inputs) on the RF accuracy. The results showed that the RF models were able to select the best forecast only in the very short range using input information regarding the significant wave height, wave direction and period, and ensemble spread. At forecast day 5 and beyond, the RF models could not determine the best wave forecast with high accuracy; the feature space presented no clear pattern to allow for successful classification. The challenges and limitations of such RF predictions for longer forecast ranges are discussed in order to support future studies in this area.

Research on Method of Trajectory Prediction in Aircraft Flight Based on Aircraft Performance and Historical Track Data

Traditional 4D trajectory prediction based on aircraft performance models and flight procedures does not consider control handover rules. Meanwhile, method based on historical data mining cannot accurately couple with real-time conditions such as weather and also cause computational efficiency problems. This project collected a large amount of historical data to form a control experience database and mined the historical database to obtain control experience and flight intention. On the basis of the traditional aircraft performance model, this paper puts forward the aircraft maneuver mode using strategy and introduces the high-altitude wind information from the weather information into the aircraft 4D model to optimize the aircraft 4D trajectory calculation model. By comparing the flight forecast time with the real crossing time, it is found that the average error of the improved 4D forecast crossing time is less than 5% of the flight time, which is obviously better than that before optimization. It is proved that the optimized method based on historical track data is effective and reliable, and the accuracy of 4D track prediction is improved greatly.