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Forecasting food prices play an important role in livestock and agriculture to maximize profits and minimizing risks. An accurate food price prediction model can help the government which leads to optimization of resource allocation. This paper uses ridge regression as an approach for forecasting with many predictors that are related to the target variable. Ridge…

Recently, numerous scholars have suggested fuzzy time series (FTS) models to forecast many different fields. One of the vital issues for high accurate forecasting in FTS model is method of partitioning in Universe of discourse (UoD). In this research, we propose a novel FTS model, which is established by using hedge algebra (HA) and particle…

Modulation type classification is a part of waveform estimation required to employ spectrum sharing scenarios like dynamic spectrum access that allow more efficient spectrum utilization. In this work multiple classification features, feature extraction, and classification algorithms for modulation type classification have been studied and compared in terms of classification speed and accuracy to suggest the…

With the rapid growth of financial markets, analyzers are paying more attention on predictions. Stock data are time series data, with huge amounts. Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative. There are various statistical models for…

Classic surgical skill evaluation is performed by an expert surgeon examining an apprentice in a hospital operating room. This method suffers from being subjective and expensive. As surgery becomes more complex and specialized, there is an increase need for an automated surgical skill evaluation system that is more objective and determines more exactly the skills…

The rapid advancement of the Internet of Things (IoT) is distinguished by heterogeneous technologies that provide cutting-edge services across a range of application domains. However, by eavesdropping on encrypted WiFi network traflc, attackers can infer private information such as the types and working status of IoT devices in a business or residential home. Moreover, since…

The false nearest neighbors (FNN) method estimates the variables of a system by sequentially embedding a time series into a higher-dimensional delay coordinate system and finding an embedding dimension in which the neighborhood of the delay coordinate vector in the lower dimension does not extend into the higher, that is, a dimension in which no…

Real-world applications modelled by time-dependent dynamical systems with specific properties such as long-range dependence or self-similarity are usually described by fractional Brownian motion. The investigation of the qualitative behaviour of its realisations is an important topic. For this purpose, efficient mappings from realisations of the dynamical system, i.e., time series, to a set of scalar-valued…

Forecasting the gold price movement’s volatility has essential applications in areas such as risk management, options pricing, and asset allocation. The multivariate model is expected to generate more accurate forecasts than univariate models in time series data like gold prices. Multivariate analysis is based on observation and analysis of more than one statistical variable at…

Solar energy becomes widely used in the global power grid. Therefore, enhancing the accuracy of solar energy predictions is essential for the efficient planning, managing and operating of power systems. To minimize the negatives impacts of photovoltaics on electricity and energy systems, an approach to highly accurate and advanced forecasting is urgently needed. In this…

Normalized Metered Energy Consumption (NMEC) is a solution for investors in determining the best energy-saving strategy for buildings. But on the other hand, investors need a fast and reliable evaluation results in measuring how effective the savings methods they use without wasting money. To address this issue, we selected Facebook’s latest predictive time series method…

A gas sensor array is used for gas analysis to aid in an inspection. The signals from the sensor array are fed into machine learning models for learning and classification. These signals are characterized by time series fluctuating according to the environment or drift. When an unseen pattern is entered, the classification may be incorrect,…

The Gembong reservoir in Pati Regency, Java, Indonesia is a rain-fed reservoir, which experiences a depletion of it carrying capacity. The characteristic of local rainfall is one of the important factors in assessing the potential of hydrometeorology disasters in its area. Sedimentation in watersheds and reservoirs has covered water sources, so local rainfall determines the…

Bull service is the natural copulation by a purebred male carabao with a female counterpart. This is part of the bull loan agenda of the Philippine Carabao Center-Visayas State University (PCC-VSU), one of the 12 regional centers of PCC. For the past years, PCC-VSU used averaging of bull services count of previous years and sometimes…

Source parameters of tsunami waves are an essential part of any modern tsunami warning system. Recalculation of a measured time series (wave profile obtained by a seabed-based pressure sensor) in terms of initial sea surface displacement at tsunami source is among the most (or) one of the promising approaches to be applied in a warning…

In recent years, deep learning has been widely used for time series prediction. Deep learning model that is most often used for time series prediction is LSTM. LSTM is widely used because of its excellence in remembering very long sequences. However, doing training on models that use LSTM requires a long time. Trying from one…

Short-term load forecasting (STLF) plays an important role in building business strategies, ensuring reliability and safe operation for any electrical system. There are many different methods, including: regression models, time series, neural networks, expert systems, fuzzy logic, machine learning and statistical algorithms used for short-term forecasts. However, the practical requirement is how to minimize the…

Climatic changes have a significant impact on many real life processes. Climacteric position of Albania makes precipitations and water inflows in HPP the main variables influencing the amount of electric energy produced in the country. Taking into account their volatility it has considerably increased the need of using hybrid models to improve the quality of…

The discovery of semantic information from complex signals is a task concerned with connecting humans’ perceptions and/or intentions with the signals content. In the case of audio signals, complex perceptions are appraised in a listener’s mind, that trigger affective responses that may be relevant for well-being and survival. In this paper we are interested in…

A new method to detect heart sound that does not require machine learning is proposed. The heart sound is a time series event which is generated by the heart mechanical system. From the analysis of heart sound S-transform and the understanding of how heart works, it can be deducted that each heart sound component has…

Hasty software development can produce immediate implementations with source code unnecessarily complex and hardly readable. These small kinds of software decay generate a technical debt that could be big enough to seriously affect future maintenance activities. This work presents an analysis technique for identifying architectural technical debt related to non-uniformity of naming patterns; the technique…

The determination of length of survival, or prognosis, is often viewed through statistical hazard models or with respect to a future reference time point in a classification approach (e.g., survival after 2 or 5 years). In this research, regression was used to determine a patient’s prognosis. Also, multiple behavioral representations of clinical data, including difference…

The research will explore the challenges of using local water sources inside the city for a self-sufficient urban water supply by developed a system dynamics model. This study aims to evaluate and understand the Pesanggrahan River appropriateness as a raw drinking water source through a conceptual model that can accurately represent the interactions between the…

Global warming and Climate change are commonly acknowledged as the most noteworthy environmental quandary the world is undergoing today. Contemporary studies have revealed that the Earth’s surface air temperature has augmented by 0.6°C – 0.8°C in the course of the 20th century, together with alterations in the hydrological cycle. This study focuses on detecting trends in…