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Forecasting, Volume 1, Issue 1 (December 2018)

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Open AccessArticle Oil Market Efficiency under a Machine Learning Perspective
Forecasting 2018, 1(1), 157-168; https://doi.org/10.3390/forecast1010011
Received: 17 September 2018 / Revised: 10 October 2018 / Accepted: 11 October 2018 / Published: 13 October 2018
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Abstract
Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the
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Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables that are often used in the relevant literature. Next, through a selection process, we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market. Full article
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Open AccessArticle Fourier Analysis of Cerebral Metabolism of Glucose: Gender Differences in Mechanisms of Color Processing in the Ventral and Dorsal Streams in Mice
Forecasting 2018, 1(1), 135-156; https://doi.org/10.3390/forecast1010010
Received: 19 July 2018 / Revised: 21 September 2018 / Accepted: 27 September 2018 / Published: 30 September 2018
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Abstract
Conventional imaging methods could not distinguish processes within the ventral and dorsal streams. The application of Fourier time series analysis was helpful to segregate changes in the ventral and dorsal streams of the visual system in male and female mice. The present study
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Conventional imaging methods could not distinguish processes within the ventral and dorsal streams. The application of Fourier time series analysis was helpful to segregate changes in the ventral and dorsal streams of the visual system in male and female mice. The present study measured the accumulation of [18F]fluorodeoxyglucose ([18F]FDG) in the mouse brain using small animal positron emission tomography and magnetic resonance imaging (PET/MRI) during light stimulation with blue and yellow filters, compared to during conditions of darkness. Fourier analysis was performed using mean standardized uptake values (SUV) of [18F]FDG for each stimulus condition to derive spectral density estimates for each condition. In male mice, luminance opponency occurred by S-peak changes in the sub-cortical retino-geniculate pathways in the dorsal stream supplied by ganglionic arteries in the left visual cortex, while chromatic opponency involved C-peak changes in the cortico-subcortical pathways in the ventral stream perfused by cortical arteries in the left visual cortex. In female mice, there was resonance phenomenon at C-peak in the ventral stream perfused by the cortical arteries in the right visual cortex during luminance processing. Conversely, chromatic opponency caused by S-peak changes in the subcortical retino-geniculate pathways in the dorsal stream supplied by the ganglionic arteries in the right visual cortex. In conclusion, Fourier time series analysis uncovered distinct mechanisms of color processing in the ventral stream in males, while in female mice color processing was in the dorsal stream. It demonstrated that computation of colour processing as a conscious experience could have a wide range of applications in neuroscience, artificial intelligence and quantum mechanics. Full article
(This article belongs to the Special Issue ITISE 2018: International Conference on Time Series and Forecasting)
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Open AccessArticle ARIMA Time Series Models for Full Truckload Transportation Prices
Forecasting 2018, 1(1), 121-134; https://doi.org/10.3390/forecast1010009
Received: 14 September 2018 / Revised: 21 September 2018 / Accepted: 24 September 2018 / Published: 26 September 2018
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Abstract
The trucking sector in the United States is a $700 billion plus a year industry and represents a large percentage of many firms’ logistics spend. Consequently, there is interest in accurately forecasting prices for truck transportation. This manuscript utilizes the autoregressive integrated moving
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The trucking sector in the United States is a $700 billion plus a year industry and represents a large percentage of many firms’ logistics spend. Consequently, there is interest in accurately forecasting prices for truck transportation. This manuscript utilizes the autoregressive integrated moving average (ARIMA) methodology to develop forecasts for three time series of monthly archival trucking prices obtained from two public sources—the Bureau of Labor Statistics (BLS) and Truckstop.com. BLS data cover January 2005 through August 2018; Truckstop.com data cover January 2015 through August 2018. Different ARIMA models closely approximate the observed data, with coefficients of variation of the root mean-square deviations being 0.007, 0.040, and 0.048. Furthermore, the estimated parameters map well onto dynamics known to operate in the industry, especially for data collected by the BLS. Theoretical and practical implications of these findings are discussed. Full article
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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