Application of artificial neural networks in sales forecasting

The aim of the work presented in this paper is to forecast sales volumes as accurately as possible and as far into the future as possible. The choice of network topology was Silva's adaptive backpropagation algorithm and the network architectures were selected by genetic algorithms (GAs). The networks were trained to forecast from 1 month to 6 months in advance and the performance of the network was tested after training. The test results of artificial neural networks (ANNs) are compared with the time series smoothing methods of forecasting using several measures of accuracy. The outcome of the comparison proved that the ANNs generally perform better than the time series smoothing methods of forecasting. Further recommendations resulting from this paper are presentedpublished_or_final_versio

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Understanding the Impacts of Mesosphere and Lower Thermosphere on Thermospheric Dynamics and Composition

The Earth’s Ionosphere and Thermosphere (IT) is a highly dynamic system persistently driven by variable forcings both from above (Solar EUV and the magnetosphere) and the lower atmosphere. The forcing from below accounts for the majority of the variability at low- and mid-latitude IT region during geomagnetic quiet times. The IT region is particularly sensitive to the composition, winds, and temperature of the Mesosphere and Lower Thermosphere (MLT) state. The goal of this dissertation is to help understand how the MLT region controls the upper atmosphere. This is achieved by using the IT model, Global Ionosphere Thermosphere Model (GITM) and altering its lower boundary (which is in the MLT) to allow a more accurate representation of the lower atmospheric physics within the model. At the beginning of this thesis, it is identified that recent solstitial observations of MLT atomic oxygen (O) from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument show larger densities in the summer hemisphere than in the winter hemisphere. This is opposite to what has been previously known and specified in the IT models, and its cause is still under investigation. The first study focuses on understanding the influence of this latitudinal distribution by using a more realistic specification of MLT [O] from the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X), in GITM. This study shows that despite being a minor species throughout the lower thermosphere, reversing the [O] distribution affects the pressure gradients, winds, temperature, and N2 in the lower thermosphere. These changes then map to higher altitudes through diffusive equilibrium, improving the agreement between GITM O/N2 and Global Ultraviolet Imager (GUVI) measurements. Secondly, the importance of MLT variations on the thermospheric and ionospheric semiannual variation (T-I SAO) is investigated. This is done by analyzing the sensitivity of T-I SAO in GITM to different lower boundary assumptions. This study reveals that the primary driver of T-I SAO is the thermospheric spoon mechanism, as a significant T-I SAO is reproduced in GITM without an SAO variation in the MLT. However, using a more realistic MLT [O] from WACCM-X produces an oppositely-phased T-I SAO, maximizing at solstices, disagreeing with the observations. Since the MLT [O] distribution is correct in WACCM-X, the results hint at incomplete specification/physics for lower thermospheric dynamics in GITM that can drive the transition of the SAO to its correct phase. These mechanisms warrant further investigation and may include stronger winter-to-summer winds, and lower thermospheric residual circulation. The goal of the last study is to examine the effects of spatially non-uniform turbulent mixing in the MLT on the IT system. This is achieved by introducing latitudinal variation in the eddy diffusion parameter (Kzz) in GITM. The results reveal larger spatial variability in O/N2 and TEC. However, the net effect is small (within 2-4%) on the globally averaged quantities and depends on the area of the turbulent patch. The results also show a different response between the summer and the winter IT region, with winter exhibiting larger changes. Overall, this thesis has highlighted some of the outstanding questions in the domain of lower atmosphere-IT coupling and have answered them through exhaustive comparisons of GITM simulations with different satellite observations, and extensive term analyses of the GITM equations, while laying out a framework for coupling of GITM with WACCM-X.PHDClimate and Space Sciences and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169766/1/garimam_1.pd