PERAMALAN INDEKS HARGA SAHAM MENGGUNAKAN ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD)

Stock price fluctuations make investors tend to hesitate to invest in stock markets because of an uncertain situation in the future. One method that can solve these problems is to use forecasting about the stock prices in the future. Generally, the huge size of data non linear and non stationary, and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process. One of decomposition method in time series data is Ensemble Empirical Mode Decomposition (EEMD). EEMD is process decomposition data into several Intrinsic Mode Function (IMF) and the IMF residue. In this research, this concept applied to data Stock Price Index in Property, Real Estate, and Construction from July 1, 2019 to July 30, 2020 as many as 272 data. Based on the results of data processing, as many as 6 IMF and IMF remaining were used as IMF forecasting and the IMF remaining in the future. The forecast was performed by choosing the best model of each IMF component and IMF remaining, used ARIMA and polynomial trend. Keywords: Time Series Data, Stock Price Index, EEMD, ARIMA, Polynomial Trend.

Elucidating climatic controls of polynomial trends in interannual variations of northern ecosystem productivities

<p>Rapid warming in northern high latitudes during the past two decades may have profound impacts on the structures and functioning of ecosystems. Understanding how ecosystems respond to climatic change is crucial for the prediction of climate-induced changes in plant phenology and productivity. Here we investigate spatial patterns of polynomial trends in ecosystem productivity for northern (> 30 °N) biomes and their relationships with climatic drivers during 2000–2018. Based on a moderate resolution (0.05°) of satellite data and climate observations, we quantify polynomial trend types and change rates of ecosystem productivities using plant phenology index (PPI), a proxy of gross primary productivity (GPP), and a polynomial trend identification scheme (Polytrend). We find the yearly-integrated PPI (PPI<sub>INT</sub>) shows a high degree of agreement with an OCO-2-based solar‐induced chlorophyll fluorescence GPP product (GOSIF-GPP) for distinct spatial patterns of trend types of ecosystem productivities. The averaged slope for linear trends of GPP is found positive across all the biomes, among which deciduous broadleaved and evergreen needle-leaved forests show the highest and lowest rates respectively. The evergreen needle-leaved forests, low shrub, and permanent wetland show linear trends in PPI<sub>INT</sub> over more than 50% of the covered area and permanent wetland also shows a large fraction of the area with the quadratic and cubic trends. Spatial patterns of linear trends for growing season sum of temperature, precipitation, and photosynthetic active radiation have been quantified. Based on the partial correlations between PPI<sub>INT</sub> and climate drivers, we found that there is a consistent shift of dominant drivers from temperature or radiation to precipitation across all the biomes except the permeant wetland when the trend type of ecosystem productivity changes from linear to non-linear. This may imply precipitation changes in recent years may determine the linear or non-linear responses of ecosystem productivity to climate change. Our results highlight the importance of understanding how changes in climatic drivers may affect the overall responses of ecosystems productivity. Our findings will facilitate the sustainable management of ecosystems accounting for the resilience of ecosystem productivity and phenology to future climate change.</p>

FAILURE SIMULATION OF ELEMENTS OF THE CITY ELECTRIC NETWORK (10 KV) BY THE EXAMPLE OF THE PRAVOBEREZHNIY DISTRICT OF THE IRKUTSK CITY

The paper presents mathematical models for probabil-istic evaluation and forecasting of emergency shutdowns in electric networks by the example of the Pravoberezhniy District of the Irkutsk City from 2008 through 2017. At the first stage, the autocorrelation function of the parameter series is determined to estimate its randomness. According to the calculated statistical parameters and the consent criteria, a number of equipmentfailures may be described by a three-parameter gamma distribution. A method of two-level identification of extreme (maximum and minimum) values of the parameter under study is proposed; accord-ing to this method, a significant polynomial trend is ob-tained for predicting the largest number of failures. The evaluation of the presence of trends based on monthly data showed that polynomial and power trends may be used to predict failures on electric networks. At the same time, sig-nificant trends were identifiedonly for January, February, May and December. At the next stage, trend-seasonal models are constructed; the least squares method is used to calculate their components. According to the obtained seasonality indices, the greatest increase in emergency shutdowns takes place in April and July, and a decrease -in February and March. On the basis of the correlation-regression model, factor models of failures of electrical network elements and the accumulated average daily tem-peratures for months and time are constructed. Linear and nonlinear models with and without trends are obtained. To evaluate the accuracy of the forecasts of the obtained models, the results of the retrospective forecast for 2017 were compared with the actual values. According to the resultsobtained for predicting failures on electric networks in February, June, July and September, the best result is shown by a trend-seasonal model, in May -a polynomial trend, in November -a factorial one taking into account time, in March, May and October-a nonlinear regression equation, and in December -a power trend. There are no qualitative forecast models for the months of January, April, and August. In this regard, the values of emergency shut-downs on these months may be estimated using a proba-bilistic model.

DIGITAL FORECASTING ANALYTICAL BUSINESS MODEL OF AGRICULTURAL ORGANIZATIONS IN THE SARATOV REGION

The introduction of digital technologies into the national economy is intended to accelerate the implementation of exchanges that take place at all stages of manufacturing a particular type of product (service), starting with its production and ending with the sale to the end consumer, which can significantly increase the efficiency of performing tasks while saving time. When studying a number of scientific sources, the authors found that structured and unstructured data sets, as well as a set of approaches, methods and tools for collecting, storing, transferring and transforming them into a certain result (numerical, graphic, etc.) belong to the category Big data, which is one of the key digital technologies. Thus, it has been determined that a certain amount of information, as well as the methods of its processing with the help of software products directly on the computer (construction of economic and mathematical models, graphs, etc.) are a digital object. Profit is a key goal of the functioning of a commercial organization, and forecast plays a significant role both at the stage of initial business planning and in the framework of current activities, which enables the head to make management decisions in a timely manner and adjust the vector of his development. In this regard, the purpose of the study is to develop a digital model that makes it possible to predict the financial results of the activities of economic entities in the region. The authors proposed a digital predictive and analytical model for the functioning of agricultural organizations in the Saratov region, aimed at studying production and financial indicators in dynamics over 10 years, which is based on the method of constructing a polynomial trend line of the second degree and the equations of factors affecting profit (cost, price of a unit sold products, marketability level). The paper presents two versions of the forecast results, the first of which assumes the possibility of maintaining production volumes in the future, and the second takes into account potential changes in this indicator in future periods. In the framework of the study, standard formulas for the equation of the polynomial trend line of the second degree were used, while the role of the variable is played by the analysis period in conjunction with the forecast step. The results of the study are reflected both as a whole for agricultural organizations of the Saratov region, and on the example of individual economic entities of the region: “ZAO PZ Trudovoe”, “OOO Nashe Delo” (Marks district); “OOO Leto 2002”, “OOO Vozrozhdenie- 1” (Tatishchevo district).

Detrending the Waveforms of Steady-State Vowels

Steady-state vowels are vowels that are uttered with a momentarily fixed vocal tract configuration and with steady vibration of the vocal folds. In this steady-state, the vowel waveform appears as a quasi-periodic string of elementary units called pitch periods. Humans perceive this quasi-periodic regularity as a definite pitch. Likewise, so-called pitch-synchronous methods exploit this regularity by using the duration of the pitch periods as a natural time scale for their analysis. In this work, we present a simple pitch-synchronous method using a Bayesian approach for estimating formants that slightly generalizes the basic approach of modeling the pitch periods as a superposition of decaying sinusoids, one for each vowel formant, by explicitly taking into account the additional low-frequency content in the waveform which arises not from formants but rather from the glottal pulse. We model this low-frequency content in the time domain as a polynomial trend function that is added to the decaying sinusoids. The problem then reduces to a rather familiar one in macroeconomics: estimate the cycles (our decaying sinusoids) independently from the trend (our polynomial trend function); in other words, detrend the waveform of steady-state waveforms. We show how to do this efficiently.

Peramalan Jumlah Populasi Sapi Potong di Kalimantan Selatan Menggunakan Metode Moving Average, Exponential Smoothing dan Trend Analysis

Perkembangan produksi daging sapi di Indonesia selama 30 tahun terakhir secara umum cenderung meningkat. Kebutuhan daging sapi di Indonesia masih belum bisa dicukupi oleh supply domestik, sehingga diperlukan impor daging sapi dari luar negeri. Diperlukan kajian tentang proyeksi ketersediaan populasi sapi potong di masa mendatang agar diambil kebijakan yang tepat dalam menjaga stabilitas dan keterpenuhan supply daging nasional. Penelitian ini bertujuan untuk melakukan peramalan jumlah populasi sapi potong menggunakan 3 (tiga) metode peramalan yaitu metode moving average, exponential smoothing dan trend analysis. Hasil peramalan ini selanjutnya diukur akurasinya menggunakan MAD (Mean Absolud Deviation), MSE (Mean Squared Error) dan MAPE (Mean Absolute Percentage Error). Proyeksi populasi sapi potong pada tahun 2019 (periode berikutnya) menggunakan 3 metode peramalan adalah: 195.100 (moving average); 218.225 (exponential smooting) dan 262.899 (trend analysis). Pengukuran akurasi menggunakan MAD, MSE dan MAPE menunjukkan bahwa metode peramalan jumlah populasi sapi potong yang paling akurat adalah peramalan menggunakan metode polynomial trend analysis (MAD 14.716,12; MSE 327.282.084,17; dan MAPE 0,09) karena memiliki tingkat kesalahan yang lebih kecil dibandingkan hasil peramalan menggunakan metode moving average dan exponential smoothing.