Table of Contents

Forecasting, Volume 1, Issue 1 (December 2018)

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-8
Export citation of selected articles as:
Open AccessArticle Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System
Forecasting 2018, 1(1), 107-120; https://doi.org/10.3390/forecast1010008
Received: 17 July 2018 / Revised: 13 September 2018 / Accepted: 13 September 2018 / Published: 17 September 2018
PDF Full-text (1747 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this
[...] Read more.
In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria. Full article
Figures

Figure 1

Open AccessArticle Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming
Forecasting 2018, 1(1), 90-106; https://doi.org/10.3390/forecast1010007
Received: 17 August 2018 / Revised: 10 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
PDF Full-text (1398 KB) | HTML Full-text | XML Full-text
Abstract
This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays.
[...] Read more.
This study explores the forecasting ability of two powerful non-linear computational methods: artificial neural networks and genetic programming. We use as a case of study the monthly international tourism demand in Spain, approximated by the number of tourist arrivals and of overnight stays. The forecasting results reveal that non-linear methods achieve slightly better predictions than those obtained by a traditional forecasting technique, the seasonal autoregressive integrated moving average (SARIMA) approach. This slight forecasting improvement was close to being statistically significant. Forecasters must judge whether the high cost of implementing these computational methods is worthwhile. Full article
Figures

Figure 1

Open AccessArticle Forecasting the Effects of In-Store Marketing on Conversion Rates for Online Shops
Forecasting 2018, 1(1), 70-89; https://doi.org/10.3390/forecast1010006
Received: 14 July 2018 / Revised: 26 August 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
PDF Full-text (363 KB) | HTML Full-text | XML Full-text
Abstract
As webstores usually face the issue of low conversion rates, finding ways to effectively increase them is of special interest to researchers and practitioners alike. However, to the best of our knowledge, no one has yet empirically investigated the usefulness of various in-webstore
[...] Read more.
As webstores usually face the issue of low conversion rates, finding ways to effectively increase them is of special interest to researchers and practitioners alike. However, to the best of our knowledge, no one has yet empirically investigated the usefulness of various in-webstore marketing tools like coupons or different types of product recommendations. By analysing clickstream data for a shoe and a bed online store, we are contributing to closing this gap. In particular, we use our present data to build more general hypotheses on how such purchasing incentives might function and on how they could be used in practice. Full article
Figures

Figure 1

Open AccessArticle Improved Brain Tumor Segmentation via Registration-Based Brain Extraction
Forecasting 2018, 1(1), 59-69; https://doi.org/10.3390/forecast1010005
Received: 9 July 2018 / Revised: 17 August 2018 / Accepted: 5 September 2018 / Published: 12 September 2018
PDF Full-text (7109 KB) | HTML Full-text | XML Full-text
Abstract
Automated brain tumor segmenters typically run a “skull-stripping” pre-process to extract the brain from the 3D image, before segmenting the area of interest within the extracted volume. We demonstrate that an effective existing segmenter can be improved by replacing its skull-stripper component with
[...] Read more.
Automated brain tumor segmenters typically run a “skull-stripping” pre-process to extract the brain from the 3D image, before segmenting the area of interest within the extracted volume. We demonstrate that an effective existing segmenter can be improved by replacing its skull-stripper component with one that instead uses a registration-based approach. In particular, we compare our automated brain segmentation system with the original system as well as three other approaches that differ only by using a different skull-stripper—BET, HWA, and ROBEX: (1) Over scans of 120 patients with brain tumors, our system’s segmentation accuracy (Dice score with respect to expert segmentation) is 8.6% (resp. 2.7%) better than the original system on gross tumor volumes (resp. edema); (2) Over 103 scans of controls, the new system found 92.9% (resp. 57.8%) fewer false positives on T1C (resp. FLAIR) volumes. (The other three methods were significantly worse on both tasks). Finally, the new registration-based approach is over 15% faster than the original, requiring on average only 178 CPU seconds per volume. Full article
Figures

Figure 1

Open AccessArticle Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data
Forecasting 2018, 1(1), 47-58; https://doi.org/10.3390/forecast1010004
Received: 17 June 2018 / Revised: 17 July 2018 / Accepted: 31 July 2018 / Published: 6 August 2018
PDF Full-text (439 KB) | HTML Full-text | XML Full-text
Abstract
Increased Intracranial Pressure (ICP) is a serious and often life-threatening condition. If the increased pressure pushes on critical brain structures and blood vessels, it can lead to serious permanent problems or even death. In this study, we propose a novel regression model to
[...] Read more.
Increased Intracranial Pressure (ICP) is a serious and often life-threatening condition. If the increased pressure pushes on critical brain structures and blood vessels, it can lead to serious permanent problems or even death. In this study, we propose a novel regression model to forecast ICP episodes in children, 30 min in advance, by using the dynamic characteristics of continuous intracranial pressure, vitals and medications during the last two hours. The correlation between physiological parameters, including blood pressure, respiratory rate, heart rate and the ICP, is analyzed. Linear regression, Lasso regression, support vector machine and random forest algorithms are used to forecast the next 30 min of the recorded ICP. Finally, dynamic features are created based on vitals, medications and the ICP. The weak correlation between blood pressure and the ICP (0.2) is reported. The Root-Mean-Square Error (RMSE) of the random forest model decreased from 1.6 to 0.89% by using the given medication variables in the last two hours. The random forest regression gave an accurate model for the ICP forecast with 0.99 correlation between the forecast and experimental values. Full article
Figures

Figure 1

Open AccessArticle A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets
Forecasting 2018, 1(1), 26-46; https://doi.org/10.3390/forecast1010003
Received: 18 June 2018 / Revised: 3 July 2018 / Accepted: 9 July 2018 / Published: 12 July 2018
PDF Full-text (3730 KB) | HTML Full-text | XML Full-text
Abstract
Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors
[...] Read more.
Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods. Full article
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems)
Figures

Figure 1

Open AccessArticle Effects of the Swiss Franc/Euro Exchange Rate Floor on the Calibration of Probability Forecasts
Forecasting 2018, 1(1), 3-25; https://doi.org/10.3390/forecast1010002
Received: 26 March 2018 / Revised: 23 April 2018 / Accepted: 26 April 2018 / Published: 2 May 2018
PDF Full-text (3052 KB) | HTML Full-text | XML Full-text
Abstract
Probability forecasts of the Swiss franc/euro (CHF/EUR) exchange rate are generated before, surrounding and after the placement of a floor on the CHF/EUR by the Swiss National Bank (SNB). The goal is to determine whether the exchange rate floor has a positive, negative
[...] Read more.
Probability forecasts of the Swiss franc/euro (CHF/EUR) exchange rate are generated before, surrounding and after the placement of a floor on the CHF/EUR by the Swiss National Bank (SNB). The goal is to determine whether the exchange rate floor has a positive, negative or insignificant effect on the calibration of the probability forecasts from three time-series models: a vector autoregression (VAR) model, a VAR model augmented with the LiNGAM causal learning algorithm, and a univariate autoregressive model built on the independent components (ICs) of an independent component analysis (ICA). Score metric rankings of forecasts and plots of calibration functions are used in an attempt to identify the preferred time-series model based on forecast performance. The study not only finds evidence that the floor on the CHF/EUR has a negative impact on the forecasting performance of all three time-series models but also that the policy change by the SNB altered the causal structure underlying the six major currencies. Full article
Figures

Figure 1

Open AccessFeature PaperEditorial Forecasting: A New Open Access Journal Dealing with Time Series Analysis and Forecasting
Forecasting 2018, 1(1), 1-2; https://doi.org/10.3390/forecast1010001
Received: 5 March 2018 / Revised: 6 March 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
PDF Full-text (151 KB) | HTML Full-text | XML Full-text
Abstract
Welcome to Forecasting, a new, online, open access journal, which provides an advanced forum for studies related to forecasting: theoretical, practical, computational and methodological.[...] Full article
Back to Top