Forecasting with Big Data: A Review

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data

First Measurement of the Hubble Constant from a Dark Standard Siren using the Dark Energy Survey Galaxies and the LIGO/Virgo Binary-Black-hole Merger GW170814

We present a multi-messenger measurement of the Hubble constant H 0 using the binary–black-hole merger GW170814 as a standard siren, combined with a photometric redshift catalog from the Dark Energy Survey (DES). The luminosity distance is obtained from the gravitational wave signal detected by the Laser Interferometer Gravitational-Wave Observatory (LIGO)/Virgo Collaboration (LVC) on 2017 August 14, and the redshift information is provided by the DES Year 3 data. Black hole mergers such as GW170814 are expected to lack bright electromagnetic emission to uniquely identify their host galaxies and build an object-by-object Hubble diagram. However, they are suitable for a statistical measurement, provided that a galaxy catalog of adequate depth and redshift completion is available. Here we present the first Hubble parameter measurement using a black hole merger. Our analysis results in ${H}_{0}={75}_{-32}^{+40}\,\mathrm{km}\,{{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1}$, which is consistent with both SN Ia and cosmic microwave background measurements of the Hubble constant. The quoted 68% credible region comprises 60% of the uniform prior range [20, 140] km s−1 Mpc−1, and it depends on the assumed prior range. If we take a broader prior of [10, 220] km s−1 Mpc−1, we find {H}_{0 {78}_{-24}^{+96}\,\mathrm{km}\,{{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1} (57% of the prior range). Although a weak constraint on the Hubble constant from a single event is expected using the dark siren method, a multifold increase in the LVC event rate is anticipated in the coming years and combinations of many sirens will lead to improved constraints on H 0

A Multiobjective Bacterial Optimization Method Based on Comprehensive Learning Strategy for Environmental/Economic Power Dispatch

International Conference in Swarm Intelligence, ICSI 2016, Indonesia, 25-30 June 2016This article extends the bacterial foraging optimization (BFO) for addressing the multi-objective environmental/economic power dispatch (EED) problem. This new approach, abbreviated as MCLBFO, is proposed based on the comprehensive learning strategy to improve the search capability of BFO for the optimal solution. Besides, the fitness survival mechanism based on a health sorting technique is employed and embedded in reproduction mechanism to enhance the quality of the bacteria swarm. The diversity of the solutions is achieved by the combination of two typical techniques, i.e. non-dominance sorting and crowded distance. Experimental tests on the stan-dard IEEE 30-bus, 6-generator test system demonstrate that the novel algorithm, MCLBFO, is superior to other well developed methods such as MOEA/D, SMS-EMOA and FCPSO. The results of the comparison indicate that MCLBFO is outstanding in handling optimization problems with multiple conflicting objectives.Department of Mechanical Engineerin