In Malaysia, Air Pollution Index (API) is used to assess the status of background air quality. The computation API involved six major pollutants including PM10, PM2.5, O3, CO, SO2 and NOx. Due harmful effect pollution, forecasting important. This paper introduces application Functional Time Series (FTS) model in monthly diurnal maximum curves at two selected sites Peninsular Malaysia; namely Sh...

Electricity load forecasting is a key task in the planning and operation of power systems and electricity markets, and its importance increases with the advent of smart grids. In this paper, we present AWNN, a new approach for very short-term load forecasting. AWNN decomposes the complex electricity load data into components with different frequencies that are predicted separately. It uses an a...

We introduce and discuss a local method to learn one-step-ahead predictors for iterated time series forecasting. For each single one-step-ahead prediction, our method selects among diierent alternatives a local model representation on the basis of a local cross-validation procedure. In the literature , local learning is generally used for function estimation tasks which do not take temporal beh...

We study the predictive power of autoregressive moving average models when forecasting demand in two shared computational networks, PlanetLab and Tycoon. Demand in these networks is very volatile, and predictive techniques to plan usage in advance can improve the performance obtained drastically. Our key finding is that a random walk predictor performs best for one-step-ahead forecasts, whereas...

Existing approaches to long term time series forecasting are based either on iterated one-step-ahead predictors or direct predictors. In both cases the modeling techniques which are used to implement these predictors are multi-input single-output techniques. This paper discusses the limits of single-output approaches when the predictor is expected to return a long series of future values and pr...

PM10 constitutes a major concern for Milan air quality. We presents a series of results obtained applying different neural networks approaches to the PM10 prediction problem. The 1-day ahead prediction shows a satisfactory level of accuracy, which may be further improved if a proper deseasonalization approach is adopted, thus transferring some a priori knowledge in the data pre-processing step....

There exists a wide range of paradigms, and a high number of different methodologies that are applied to the problem of time series prediction. Most of them are presented as a modified function approximation problem using input/output data, in which the input data are expanded using values of the series at previous steps. Thus, the model obtained normally predicts the value of the series at a t...

We consider the problem of multistep-ahead prediction in time series analysis by using nonparametric smoothing techniques. Forecasting is always one of the main objectives in time series analysis. Research has shown that non-linear time series models have certain advantages in multistep-ahead forecasting. Traditionally, nonparametric k-step-ahead least squares prediction for non-linear autoregr...

Random forest models have recently gained popularity for economic forecasting. Earlier studies demonstrated their potential to provide early warnings of recession and serve as a competitive method older prediction models. This study offers the first evaluation random forecast Czech economy. The one-step-ahead forecasting results show high accuracy on data are proven outperform forecasts from Mi...

Abstract In this study, three machine learning techniques, the XGBoost (Extreme Gradient Boosting), LSTM (Long Short-Term Memory Networks), and ARIMA (Autoregressive Integrated Moving Average Model), are utilized to deal with time series prediction tasks for coastal bridge engineering. The performance of these techniques is comparatively demonstrated in typical cases, wave-load-on-deck under re...