A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction (FLSVR TSP ). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR's tuning parameters, the FLSVR TSP incorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that the FLSVR TSP has achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction

Hybrid regression model for near real-time urban water demand forecasting

[EN] The most important factor in planning and operating water distribution systems is satisfying consumer demand. This means continuously providing users with quality water in adequate volumes at reasonable pressure, thus ensuring reliable water distribution. In recent years, the application of statistical, machine learning, and artificial intelligence methodologies has been fostered for water demand forecasting. However, there is still room for improvement; and new challenges regarding on-line predictive models for water demand have appeared. This work proposes applying support vector regression, as one of the currently better machine learning options for short-term water demand forecasting, to build a base prediction. On this model, a Fourier time series process is built to improve the base prediction. This addition produces a tool able to eliminate many of the errors and much of the bias inherent in a fixed regression structure when responding to new incoming time series data. The final hybrid process is validated using demand data from a water utility in Franca, Brazil. Our model, being a near real-time model for water demand, may be directly exploited in water management decision-making processes. (C) 2016 Elsevier B.V. All rights reserved.This work has been partially supported by CAPES Foundation of Brazil’s Ministry of Education. The data were provided by SABESP, São Paulo state water management company.Brentan, BM.; Luvizotto, E.; Herrera Fernández, AM.; Izquierdo Sebastián, J.; Pérez García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics. 309:532-541. doi:10.1016/j.cam.2016.02.009S53254130

A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors

Accurate time series prediction techniques are becoming fundamental to modern decision support systems. As massive data processing develops in its practicality, machine learning (ML) techniques applied to time series can automate and improve prediction models. The radical novelty of this paper is the development of a hybrid model that combines a new approach to the classical Kalman filter with machine learning techniques, i.e., support vector regression (SVR) and nonlinear autoregressive (NAR) neural networks, to improve the performance of existing predictive models. The proposed hybrid model uses, on the one hand, an improved Kalman filter method that eliminates the convergence problems of time series data with large error variance and, on the other hand, an ML algorithm as a correction factor to predict the model error. The results reveal that our hybrid models obtain accurate predictions, substantially reducing the root mean square and absolute mean errors compared to the classical and alternative Kalman filter models and achieving a goodness of fit greater than 0.95. Furthermore, the generalization of this algorithm was confirmed by its validation in two different scenariosThe authors acknowledge the support provided by the companies that released the data used for the analysi

Peramalan Irradiance Cahaya Matahari pada Sel Surya untuk Memenuhi Kebutuhan Energi Listrik dengan Metode Support Vector Regression (SVR)

This paper suggests the use of support vector regression (SVR) method for forecasting irradiance of sunlight on solar cells so that the energy produced by the solar cells can be predicted to meet electricity needs. This prediction is very important because to provide electrical energy that is sustainable and has a good reliability which has the constant frequency and constant voltage. From the simulation results can be seen that the SVR method has not a fairly good prediction results. So that, the approximate energy of solar cell that can be transfered to meet the electricity needs of the next month still not accurate with this method. Future research will be tried SVR hybrid time series method.Keywords : Electrical Energy, Irradiance,Support vector regression (SVR).Abstrak— Pada tulisan ini digunakan metode Support Vector Regression (SVR) untuk peramalan irradiance cahaya matahari pada sel surya sehingga besar energi yang dihasilkan sel surya bisa diprediksi untuk memenuhi kebutuhan energi listrik. Prediksi ini sangat penting dikarena untuk menyediakan energi listrik yang berkelanjutan dan mempunyai keandalan yang baik yaitu mempunyai frekuensi konstan dan tegangan konstan. Dari hasil simulasi dapat dilihat bahwa metode SVR mempunyai hasil prediksi yang masih rendah. Sehingga perkiraan energi solar cell yang dapat dikirim untuk memenuhi kebutuhan listrik satu bulan ke depan masih belum cukup akurat dengan menggunakan metode ini. Pada penelitian mendatang, akan dicoba penggunaan metode SVR berbasis time series.Kata Kunci : Energi listrik, Irradiance, Support Vector Regression (SVR)

Highly-Accurate Electricity Load Estimation via Knowledge Aggregation

Mid-term and long-term electric energy demand prediction is essential for the planning and operations of the smart grid system. Mainly in countries where the power system operates in a deregulated environment. Traditional forecasting models fail to incorporate external knowledge while modern data-driven ignore the interpretation of the model, and the load series can be influenced by many complex factors making it difficult to cope with the highly unstable and nonlinear power load series. To address the forecasting problem, we propose a more accurate district level load prediction model Based on domain knowledge and the idea of decomposition and ensemble. Its main idea is three-fold: a) According to the non-stationary characteristics of load time series with obvious cyclicality and periodicity, decompose into series with actual economic meaning and then carry out load analysis and forecast. 2) Kernel Principal Component Analysis(KPCA) is applied to extract the principal components of the weather and calendar rule feature sets to realize data dimensionality reduction. 3) Give full play to the advantages of various models based on the domain knowledge and propose a hybrid model(XASXG) based on Autoregressive Integrated Moving Average model(ARIMA), support vector regression(SVR) and Extreme gradient boosting model(XGBoost). With such designs, it accurately forecasts the electricity demand in spite of their highly unstable characteristic. We compared our method with nine benchmark methods, including classical statistical models as well as state-of-the-art models based on machine learning, on the real time series of monthly electricity demand in four Chinese cities. The empirical study shows that the proposed hybrid model is superior to all competitors in terms of accuracy and prediction bias

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations

The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm