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Time series prediction relies on past data points to make robust predictions. The span of past data points is important for some applications since prediction will not be possible unless the minimal timespan of the data points is available. This is a problem for cyclone wind-intensity prediction, where prediction needs to be made as a cyclone is identified. This paper presents an empirical study on...
This paper presents the application of an artificial neural network to perform an analysis of the Coefficient of Performance for a compression vapor system operating with R1234yf. A testing facility was built to measure several parameters at the input and at the output of the refrigeration system. These parameters were: the compressor rotation speed, the temperature, and the volumetric flow in the...
In this paper we propose a study to identify the best ANN configuration in terms of number of neurons, number of layers, training-set size, in order to perform the day-ahead energy production forecast for a Photo-Voltaic (PV) plant. This set up is applied to a novel hybrid method (PHANN Physic Hybrid Artificial Neural Network) in order to enhance the energy day-ahead forecast combining both the deterministic...
Time series forecasting plays a key role in many areas of science, finance and engineering, mainly for the estimation of trend or seasonality of a variable under observation, aiming to serve as basis for future purchase decisions, choice of design parameters or maintenance schedule. Artificial Neural Networks (ANNs) have proven to be suitable in linear or nonlinear functions mapping. However, the...
Existing adaptive predictive methods often use multiple adaptive mechanisms as part of their coping strategy in non-stationary environments. We address a scenario when selective deployment of these adaptive mechanisms is possible. In this case, deploying each adaptive mechanism results in different candidate models, and only one of these candidates is chosen to make predictions on the subsequent data...
Anomaly detection involves identifying the events which do not conform to an expected pattern in data. A common approach to anomaly detection is to identify outliers in a latent space learned from data. For instance, PCA has been successfully used for anomaly detection. Variational autoencoder (VAE) is a recently-developed deep generative model which has established itself as a powerful method for...
Accurate forecasting of solar power is needed for the successful integration of solar energy into the electricity grid. In this paper we consider the task of predicting the half-hourly solar photovoltaic power for the next day from previous solar power and weather data. We propose and evaluate several clustering based methods, that group the days based on the weather characteristics and then build...
We propose hybrid ensemble models for time series forecasting. A hybrid ensemble combines the output of several different models by a weighted mean that forms the final forecast. The final hybrid ensemble model consists of several individual models: A nearest neighbor/trajectory ensemble model, a feed-forward neural network ensemble, a trend cycle model, an autoregressive model and an ensemble model...
Although artificial neural networks are occasionally used in forecasting future sales for manufacturing in industry, the majority of algorithms applied today are univariate statistical time series methods for level, seasonal, trend or trend-seasonal patterns. With different statistical methods created for different time series patterns, large scale applications on 10,000s of times series require automatic...
The challenging problem of forecasting a given time series as accurately as possible is reality in different areas of expertise. The requirement of achieving reliable forecasts, for assisting the new generation of soft sensors, requests the development of novel smart mechanisms to be integrated into the available forecasting models. This current paper improves the previous work of Coelho et al. [1],...
Statistical models built on historical data are often found to be effective in forecasting Indian summer monsoon. However, linear models are found to be inadequate, and non-linear models like neural networks provide better performance. In this article, we study the use of recurrent neural network for long range forecast of Indian monsoon at lead of one season. Recurrent network model the sequential...
There has been a dramatic increase in the sharing of opinions and information across different web platforms and social media, especially online product reviews. Cloud web portals, such as getApp.com, were designed to amalgamate cloud service information and to also examine how consumers evaluate their experience of using cloud computing products. The current literature shows the growing importance...
The classification of high dimensional data is an arduous task especially with the emergence of high quality data acquisition techniques. This problem is accentuated when the whole set of features is needed to learn a classifier such as the case of genomic data. The Bayesian approach is suitable for these applications because it represents graphically and statistically the dependencies between the...
Real-life behaviors shown by the mobile users typically exhibit plenty noises, making it hard to construct an effective recommendation engine. In this paper, we present a fused model based on the LR algorithm and the GBDT algorithm to recommend vertical industry commodities in a mobile setting. A set of specifically designed methods are proposed to deal with the data preprocessing and feature extraction...
This paper deals with the prediction of photoplethysmography (PPG) signal which is chaotic in nature. A sequential variant of the extreme learning machine (ELM) is shown to yield satisfactory prediction performance as per the calculated root-meansquare error (RMSE). Moreover, as a second measure of goodness, the heart rate determined from the predicted PPG signal is shown to be within limits of agreement...
Connectivity analysis has become an essential tool for the evaluation of functional brain dynamics. The functional connectivity between different parts of the brain, or between different sensors, is assumed to provide key information for the discrimination of brain responses. In this study, we propose an estimation of effective cortical connectivity measures in frontal and parietal areas of human...
The stock market is the most important institution for global investments all around the world. Among the possibles analysis, the study and forecasting of ultra-high-frequency time series is an interesting and great challenge to econometric modeling and statistical analysis due its complex behaviour. This work proposes a hybrid intelligent system to forecast ultra-high-frequency stock prices. The...
Critical for successful operations of service industries, such as telecoms, utility companies and logistic companies, is the service chain planning process. This involves optimizing resources against expected demand to maximize the utilization and minimize the wastage, which in turn maximizes revenue whilst minimizing the cost. This is increasingly involving the automation of the planning process...
Accurate prediction of time series is of great interest because it can guide decisions in many economical or industrial fields. Methods can forecast either one or several steps ahead. The former is simpler and more common in many applications, while the latter is more challenging. The literature describes mainly three strategies of multi-step-ahead prediction: iterated (repeated one-step-ahead), direct,...
Monitoring the presence of occupants in a room in a timely manner is a fundamental step for effective building management. Environmental sensor networks have the advantages of high cost-efficiency and non-intrusiveness on privacy and are very suitable for room occupancy detection. Nonlinear discriminative models, e.g., support vector machine and neural networks, have shown good detection performance...
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