Intraday forecasts of a volatility index: Functional time series methods with dynamic updating

As a forward-looking measure of future equity market volatility, the VIX index has gained immense popularity in recent years to become a key measure of risk for market analysts and academics. We consider discrete reported intraday VIX tick values as realisations of a collection of curves observed sequentially on equally spaced and dense grids over time and utilise functional data analysis techniques to produce one-day-ahead forecasts of these curves. The proposed method facilitates the investigation of dynamic changes in the index over very short time intervals as showcased using the 15-second high-frequency VIX index values. With the help of dynamic updating techniques, our point and interval forecasts are shown to enjoy improved accuracy over conventional time series models.Comment: 29 pages, 5 figures, To appear at the Annals of Operations Researc

Computer Vision System as a Tool to Estimate Pork Marbling

Currently pork marbling is assessed subjectively in the industry, because of the limited methods and tools that are suitable for the industry. In this dissertation, we are devoted to develop a computer vision system for objective measurement of pork which suits the industrial needs. Experiment 1 examined the possibility of using computer vision system (CVS) to predict marbling in a lab-based experiment using pork samples that were already trimmed of subcutaneous fat and connective tissue. Experiment 2 an industrial scale CVS was built to predict the 3rd and 10th rib pork chop’s marbling. Experiment 3 the industrial scale CVS was tested in the meat plant and images of whole boneless pork loin were collected. The CVS predicted marbling were compared with subjective marbling score using crude fat percentage (CF%) as standard. In experiment 1 subjective marbling score had a correlation of 0.81 with CF% while CVS had a 0.66 correlation. CVS has shown an accuracy of 63% for stepwise regression model and 75% for support vector machine model. These results indicate that CVS has the potential to be used as an tool to predict pork intramuscular fat (IMF)%. In experiment 2 the accuracy of CVS predicting pork chop CF% was 68.6% and subjective marbling was 70.1%. A drop of accuracy in predicting anterior chop CF% for both CVS and objective marbling score was observed when compared to posterior chop, this suggest that there is a discrepancy in accuracy between the anatomy location of samples collected. In experiment 3 the accuracy of CVS predicting boneless whole loin was 58.6% and subjective marbling score was 53.3%. In this research, CVS has demonstrated a consistency of accuracies using different pork samples. CVS has shown higher accuracy when predicting whole boneless loin IMF% when compared to subjective assessment.National Pork BoardColeman Natura

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy \u3e95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting