Time-Series

Kebutuhan  masyarakat  akan  sarana  transportasi  pada  saat  ini  sangatlah penting. Salah satu alat transportasi paling umum digunakan di Indonesia asalah sepeda motor. Pada  saat  ini  kebutuhan  sepeda  motor  bagi  masyarakat terus  mengalami  peningkatan. Merk sepeda motor yang paling laris di Indonesia adalah Honda. Meski mengalami pasang surut dalam penjualannya, tetapi Honda selalu menguasai pangsa pasar di Indonesia hingga kini. Penelitian ini bertujuan untuk mendapatkan nilai peramalan penjualan sepeda motor Honda pada satu periode kedepan yaitu bulan juli 2016. Data yang digunakan adalah data penjualan sepeda motor Honda sejak bulan januari 2008 sampai dengan juni 2016. Metode yang digunakan adalah ARIMA. Metode ARIMA merupakan suatu metode yang menghasilkan ramalan-ramalan berdasarkan sintesis dari pola data secara historis. Hasil penelitian menunjukkan bahwa model yang didapatkan adalah ARIMA (0,1,1) yang dapat digunakan untuk memprediksi penjualan sepeda motor Honda bula juli 2016.

This study examines the determinants of manufacturing sector growth in Nigeria within the time frame of 1981-2016. The main objective of this study is to identify the determinants of the manufacturing sector growth in Nigeria. A model was constructed to incorporate manufacturing output (MO) as the dependent variable, federal government allocation on power, inflation rate, trade openness, gross capital formation and primary school enrolment as the independent variables and tested using the ordinary least-square (OLS) techniques. The empirical result shows that federal government allocation on power, inflation rate and primary school enrolment have negative relationship on the manufacturing output whereas trade openness and gross capital formation have positive relationship on manufacturing output in Nigeria. From the result, federal government allocation on power, trade openness and gross capital formation are significant variables to determine manufacturing output in Nigeria whereas inflation rate and primary school enrolment are not significant variables to determine manufacturing output in Nigeria. Based on the result, the researcher recommends that Nigeria should based on reckoning diversify the income stream of the economy (rebasing the economy) so as to lower the production cost which will based on multiplier effect decrease the selling price of the domestic product.

Keywords: Domestic product, Manufacturing output, Inflation trade, Trade Openness,

Imperatively Hidden Objects/Factors (IHO/F) refers to relevant items, which we cannot perceive and measure yet. Nevertheless, they have an impact on our predicted outcome. We have created model that allows for the integration and discovery for the still unknown. If researchers would take the time to understand it, they'd design their experimental studies in a much different way because their scientific objective would shift toward feature discovery to enhance feature selection to make even better predictions with supervised machine learning algorithms.

Neste trabalho são descritas as linhas gerais de uma metodologia para previsão de carga capaz
de fornecer previsões até 48 horas à frente, com resolução temporal de 10, 15, 30 e 60 minutos. A
metodologia de previsão baseia-se no uso combinado de um modelo de lógica nebulosa para a previsão
do perfil horário da curva de carga até 48 horas á frente, de uma rede neural polinomial para prever
valores diários da demanda mínima e da demanda de ponta e da interpolação spline para obter previsões
com diferentes resoluções de tempo. O desempenho da metodologia proposta é ilustrado pelos resultados
obtidos em um experimento computacional com os registros de carga do Sistema Interligado Nacional
(SIN).

"Forecasting reservoir inflow is important to hydropower reservoir management and scheduling. An Adaptive-Network-based Fuzzy Inference System (ANFIS) is successfully developed to forecast the long-term discharges in Manwan Hydropower. Using the long-term observations of discharges of monthly river flow discharges during 1953-2003, different types of membership functions and antecedent input flows associated with ANFIS model are tested. When compared to the ANN model, the ANFIS model has shown a significant forecast improvement. The training and validation results show that the ANFIS model is an effective algorithm to forecast the long-term discharges in Manwan Hydropower. The ANFIS model is finally employed in the advanced water resource project of Yunnan Power Group.

Keywords: artificial neural-networks; time-series; identification, Long-term prediction, discharges, Manwan Hydropower, adaptive-network-based fuzzy inference systems, models"

This study was conducted to investigate the use of remote sensing data to distinguish between different rangeland sites. Three rangeland sites were selected based on soil types and variation in vegetation cover. Normalized Difference Vegetation Index and The soil-adjusted vegetation index were used. All selected rangeland sites exhibited NDVI and SAVI. Results showed that there were a number of important implications regarding using NDVI and SAVI. The results revealed that, the characteristics and different types of soils characterize different rangeland land sites as basic units for assessing special change when monitoring rangelands in semi-arid areas. Therefore, for better differentiation capacity we need to increase our understanding of vegetation structure and soil type in each range site.

Being the first stage of analysis within an image, low-level feature detection (edge and line detection) is a crucial step in the image analysis process and, as such, deserves suitable attention. This paper presents a systematic investigation into low-level feature detection in spectrogram images. The result of which is the identification of frequency tracks. Analysis of the literature identifies different strategies for accomplishing low-level feature detection. Nevertheless, the advantages and disadvantages of each are not explicitly investigated. Three model based detection strategies are outlined, each extracting an increasing amount of information from the spectrogram, and, through ROC analysis, it is shown that at increasing levels of extraction the detection rates increase. Nevertheless, further investigation suggests that model based detection has a limitation---it is not computationally feasible to fully evaluate the model of even a simple sinusoidal track. Therefore, alternative approaches, such as dimensionality reduction, are investigated to reduce the complex search space. It is shown that, if carefully selected, these techniques can approach the detection rates of model based strategies that perform the same level of information extraction.

We show that the monthly recorded history (1866–2014) of the Southern Oscillation Index (SOI), a descriptor of the El Niño Southern Oscillation (ENSO) phenomenon, can be correctly described as a dynamic system supporting a potential nonlinear predictability well beyond the spring barrier. Long-term predictability is strongly connected to a detailed knowledge about the topology of the attractor obtained by embedding the SOI index in a wavelet base state space. By utilizing the state orbits on the attractor, we show that the information contained in the SOI is sufficient to provide nonlinear attractor information, allowing the detection of predictability for longer than a year: 2, 3, and 4 years in advance throughout the record with an acceptable error. This is possible due to the fact that the lower-frequency variability of the SOI presents long-term positive autocorrelation. Thus, by using complementary methods, we confirm that the reconstructed attractor of the low-frequency part (lower than 1/year) of SOI time series cannot be attributed to stochastic influences. Furthermore, we establish its multifractality. As an example of the capabilities of the methodology, we investigate a few specific El Niño (1972–1973, 1982–1983, 1997–1998) and La Niña (1973–1974, 1988–1989 and 2010–2011) events. Our results indicate that each of these present several equivalent temporal structures over other eras of these 149 years (1866–2014). Accordingly, none of these cases, including extreme events, presents temporal singularity. We conclude that the methodology’s simplicity of implementation and ease of use makes it suitable for studying nonlinear predictability in any area where observations are similar to those describing the ENSO phenomenon.