In this research, a taxonomy of known electricity demand forecasting techniques is presented based on extensive empirical studies. In addition, a decision strategy for selecting an electricity demand forecasting method has been presented. The strategy has been formulated based on an eight-factor...

To achieve sustainable development, massive changes towards fostering a clean and pollution-reducing industrial sector are quintessential. The textile industry has been one of the main contributors to water pollution all over the world, causing more than 20% of the registered levels of water...

A method is proposed for forecasting composite time series such as the market shares for multiple brands. Its novel feature is that it relies on multi-series adaptations of exponential smoothing combined with the log-ratio transformation for the conversion of proportions onto the real line. It...

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models,...

In this study, I investigate the necessary condition for the consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally...

We assess the out-of-sample forecasting performance of eight models on Indian real exports and imports. The results, in large part show only a slight increase or no clear increase in India's international trade. However, the results are very robust when comparing the predictions in terms of...

Exponential smoothing is one of the most popular forecasting methods. We present a method for bootstrap aggregation (bagging) of exponential smoothing methods. The bagging uses a Box-Cox transformation followed by an STL decomposition to separate the time series into trend, seasonal part, and...

This article discusses Windle and Carvalho's (2014) state-space model for observations and latent variables in the space of positive symmetric matrices. The present discussion focuses on the model specification and on the contribution to the positive-value time series literature. I apply the...

Exponential smoothing models are an important prediction tool in macroeconomics, finance and business. This paper presents the analytical forecasting properties of the random coefficient exponential smoothing model in the multiple source of error framework. The random coefficient state-space...

The paper provides evidence on correlation structure forecasting techniques. We use index model parameters to forecast significant parts of securities returns volatility – systematic risk and specific risk. Except of this static perspective we have suggested, improved and dynamised these...

The article studies a possibility of use of methods of non-linear dynamics for structuring an operative forecast of complex and structured processes described by time series. It offers a model of extended logistic mapping for interpretation of characteristics of dynamic processes. It studies...

Introduction. In recent years there was a need of new theoretic and methodical approach of forecasting and updating of passenger cars. However it is necessary to develop the new principles of forecasting of volumes of passenger traffic for the solution of this question. The purpose. Main...

A key input to the call center staffing process is a forecast for the number of calls arriving. Density forecasts of arrival rates are needed for analytical call center models, which assume Poisson arrivals with a stochastic arrival rate. Density forecasts of call volumes can be used in...

The aim of this article is the accuracy evaluation of suitable models used for prediction of the unemployment rate development in Czech Republic under conditions of economic depression. Models were based on exponential smoothing and training of artificial neural networks. The most suitable...

Moving averages are one of the most popular and easy-to-use tools available to a technical analyst, and they also form the building blocks for many other technical indicators and overlays. Building a moving average (MA) model needs determining four factors of (1) approach of issuing signals, (2)...

Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. ETS (exponential smoothing) has received extensive attention in the recent years since the invention of its state space formulation....