Query: STOCKS MACHINE LEARNING

title: Large, climate sensitive soil carbon stocks mapped with pedology informed machine learning in the North Pacific coastal temperate rainforest

title: A Literature Survey on Stocks Predictions using Hybrid Machine Learning and Deep Learning Models
abstract: Prediction of stocks requires a lot of knowledge on market share values and trends. This knowledge can be obtained by experience in this particular field. For a normal human it requires a lot of time and energy to gain experience to predict trends in stock prices. With advancement in technology, machine learning algorithms keep the capability of predicting trends in stocks because of the huge computational capacity which is available nowadays. In this paper, hybrid machine learning and deep learning models have been discussed. A brief literature survey has been carried out on both machine learning and deep learning algorithms which have been used in past especially for stock predictions and analysis.  Prediction, Deep Learning, Machine Learning, Hybrid Models, Neural Networks.

title: Optimisation in machine learning: An application to topsoil organic stocks prediction in a dry forest ecosystem
abstract: Soil organic carbon (SOC) sequestration plays a key role in reducing the atmospheric greenhouse gas concentration. However, dry forest ecosystems in Ecuador are endangered to become a source of carbon emissions because of deforestation. Often spatial information, necessary to quantify potential carbon loss to the atmosphere, is missing. This particularly applies to remote areas of limited accessibility. This study aims to regionalise the SOC stocks of a small and poorly accessible dry forest ecosystem in southwestern Ecuador by using boosted regression tree (BRT) models. Resampling in a nested repeated k fold cross validation approach was applied to develop robust models for a dataset of 118 samples with limited predictor information. To select an optimal set of model parameters, optimisation by differential evolution (DE) was applied for parameter tuning. Predictor selection was implemented using the same optimisation algorithm. This study demonstrates how the predictive performance of BRT models can be improved by applying an optimisation approach for parameter tuning and predictor selection. Model performance was improved by approximately 40% concerning the R2. Still, the results also demonstrated the difficulties of machine learning applications in small and highly heterogeneous natural areas. Very variable or even random factors were assumed to distort the relationship between predictor and response variables. We assume that the presented approach is particularly successful in the case of a real valued multivariate space of tuning parameters. However, this requires testing in further machine learning applications and algorithms.

title: Machine Learning based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts
abstract: An increasing amount of companies and cities plan to become CO2 neutral, which requires them to invest in renewable energies and carbon emission offsetting solutions. One of the cheapest carbon offsetting solutions is preventing deforestation in developing nations, a major contributor in global greenhouse gas emissions. However, forest preservation projects historically display an issue of trust and transparency, which drives companies to invest in transparent, but expensive air carbon capture facilities. Preservation projects could conduct accurate forest inventories (tree diameter, species, height etc. to transparently estimate the biomass and amount of stored carbon. However, current rainforest inventories are too inaccurate, because they are often based on a few expensive ground based samples and/or low resolution satellite imagery. LiDAR based solutions, used in US forests, are accurate, but cost prohibitive, and hardly accessible in the Amazon rainforest. We propose accurate and cheap forest inventory analyses through Deep Learning based processing of drone imagery. The more transparent estimation of stored carbon will create higher transparency towards clients and thereby increase trust and investment into forest preservation projects.

title: Machine Learning Models for Forecasting of Individual Stocks Price Patterns
abstract: Stock investors always consider potential future prices before investing in any stock for making a profit. A large number of studies are found on the prediction of stock market indices. However, the focus on individual stock closing price predictions well ahead of time is limited. In this chapter, a comparative study of machine learning based models is used for the prediction of the closing price of a particular stock. The proposed models are designed using back propagation neural networks (BPNN) support vector regression (SVR) with SMOReg, and linear regression (LR) for the prediction of the closing price of individual stocks. A total of 37 technical indicators (features) derived from historical closing prices of stocks are considered for predicting the future price of stock in a time window of five days. The experiment is performed on stocks listed on Bombay Stock Exchange (BSS) India. The model is trained and tested using feature values extracted from the past five year closing price of stocks of different sectors including aviation, pharma, banking, entertainment, and IT.

title: Quantifying driving factors of vegetation carbon stocks of Moso bamboo forests using machine learning algorithm combined with structural equation model
abstract: Moso bamboo (Phyllostachys pubescens) is widely distributed in subtropical China and plays an important role in carbon cycling in terrestrial ecosystems. Knowledge of the main driving factors that affect aboveground carbon stocks in Moso bamboo forests is needed to increase carbon sequestration potential. We used a large scale database from national forest continuous inventory from 2004 to 2014 in Zhejiang, China, and combined Random Forest analysis (RF) with structural equation modeling (SEM) to quantify the contribution of main driving factors on vegetation carbon stocks in Moso bamboo forests, and to evaluate the direct and indirect total effects of the main driving factors on aboveground carbon stocks, as well as to investigate changes in the standardized total effects from 2004 to 2014. The RF model explained 84.9% of the variation in 2004, 78.8% in 2009, and 82.2% in 2014. The SEM that included the average age, average diameter at breast height (DBH) culms density, mean annual temperature (MAT) and mean annual precipitation (MAP) explained 92.5% of the variation in 2004, 88.2% in 2009, and 88.6% in 2014. The results showed that average age, average DBH, culms density, MAT, and MAP were the most crucial driving factors of vegetation carbon stocks in Moso bamboo forests. The values of standardized total effects of the main driving factors showed that the average age, average DBH, and culms density had positive effects on vegetation carbons stocks, whereas MAT and MAP had negative effects. Furthermore, the positive effects of average age on the increase of vegetation carbon stocks increased significantly, the negative effects of MAT increased with the increasing MAT, but the negative effects of MAP decreased with the increasing MAP. Overall, our study provided new insights into the sensitivity and potential response of carbon sequestration in Moso bamboo forests to structural development and climate change in Zhejiang Province.

title: Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches
abstract: Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms including the model tuning and predictor selection were compared via five repetitions of a tenfold cross validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m 2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

title: Analysis of Spectrum Occupancy Using Machine Learning Algorithms
abstract: In this paper, we analyze the spectrum occupancy in cognitive radio networks (CRNs) using different machine learning techniques. Both supervised techniques [naive Bayesian classifier (NBC) decision trees (DT) support vector machine (SVM) linear regression (LR) and unsupervised algorithms [hidden Markov model (HMM) are studied to find the best technique with the highest classification accuracy (CA) A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and the CA is performed. The classified occupancy status is further utilized to evaluate the blocking probability of secondary user for future time slots, which can be used by system designers to define spectrum allocation and spectrum sharing policies. Numerical results show that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, we proposed a new SVM algorithm by combining it with a firefly algorithm (FFA) which is shown to outperform all the other algorithms.

title: Predicting stock movement direction with machine learning: An extensive study on S&P 500 stocks
abstract: Stocks movement direction forecasting has received a lot of attention. Indeed, being able to make accurate forecasts has strong implications on trading strategies. Surprisingly enough little has been published, relatively to the importance of the topic. In this paper, we reviewed how well four classic classification algorithms: random forest, gradient boosted trees, artificial neural network and logistic regression perform in predicting 463 stocks of the S&P 500. Several experiments were conduced to thoroughly study the predictability of these stocks. To validate each prediction algorithm, three schemes we compared: standard cross validation, sequential validation and single validation. As expected, we were not able to predict stocks future prices from their past. However, unexpectedly, we were able to show that taking into account recent information such as recently closed European and Asian indexes to predict S&P 500 can lead to a vast increase in predictability. Moreover, we also found out that, among various sectors, financial sector stocks are comparatively more easy to predict than others.

title: Estimating soil organic carbon stocks using machine learning methods in the semi arid rangelands of New South Wales
abstract: Soil organic carbon (SOC) is pivotal for biological, chemical and physical processes and provides vital information on changes in soil fertility and land degradation. Rangelands, accounting for about 81% of Australian land area, represent considerable carbon storage potential. Efficient modelling techniques to evaluate the potential for rangeland SOC stocks are vitally important in the assessment for the global carbon cycle and quantum abatement. This study aimed to evaluate boosted regression trees (BRT) and random forest (RF) in predicting SOC stocks from ground measured and remotely sensed variables using two feature selection techniques to identify the dominant variables that affect SOC stocks in the rangelands. Using field based measurement of SOC stock collected from 564 sites across the study area and 28 of GISbased environmental variables including climate, topography, radiometry, vegetation and land fractional cover data, we employed stepwise regression (SR, linear approach) and genetic algorithm (GA, nonlinear approach) to select the most informative variables. These selected predictors were then used to train the BRT and RF models. In all, four models were evaluated; BRT using stepwise selection of predictors (SR_BRT) RF using stepwise (SR_RF) BRT using GA selection of predictors (GA_BRT) and RF using GA (GA_RF) In addition, BRT using all predictors (All_BRT) and the RF using all predictors (All_RF) were used as benchmarks to test the performance of the four models. Of the field based data, 75% was used to train the model "calibration dataset" and the remaining 25% was used to validate the prediction of SOC stocks "validation dataset" The results indicate that the RF exhibited a better performance in predicting SOC stocks than the BRT regardless of input variables. The two models explained ~45% of the total SOC stocks. In addition, we verified that feature selection for both machine learning techniques is necessary for estimating SOC stocks, even though BRT was relatively insensitive to the input features selected by SR. The GA_RF was the most promising model with reliable predictors to predict SOC stocks, with the lowest root mean square error (RMSE) and the highest R2 values (7.44 Mg C ha 1 and 0.48, respectively) suggesting that the proposed methodology may provide a cost effective method to predict SOC stocks in the rangelands. The important variables for explaining the observed SOC stocks were rainfall, elevation, prescott index (PI) and land fractional cover (bare ground fraction)