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Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

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Journal of Data Analysis and Information Processing 2024年第3期12卷 319-347页

作者： Amadou Kindy Barry Anthony Waititu Gichuhi lawrence nderu Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation (PAUISTI) Nairobi Kenya Department of Statistics and Actuarial Science Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Computing School and Information Technology (SCIT) Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

关键词： Spatial Heterogeneity Spatial Data Feature Selection Standardization Machine Learning Models Hybrid Models

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Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag

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Journal of Data Analysis and Information Processing 2023年第1期11卷 49-68页

作者： Luyandza Sindi Mamba Antony Ngunyi lawrence nderu Department of Mathematics Institute for Basic Sciences Technology and Innovation The Pan African University Nairobi Kenya Department of Statistics and Actuarial Sciences Dedan Kimathi University of Technology Nyeri Kenya Department of Computing School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.

关键词： Optimized Gated Recurrent Unit Approximation Coefficient Stationary Wavelet Transform Activation Function Time Lag

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A Hybrid Neural Network Model Based on Transfer Learning for Forecasting Forex Market

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Journal of Data Analysis and Information Processing 2023年第2期11卷 103-120页

作者： Salum Hassan Faru Anthony Waititu lawrence nderu Department of Mathematics The Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Department of Statistics and Actuarial Science Jomo Kenyatta University of Agriculture and Technology Juja Kenya Department of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Juja Kenya

The forecasting research literature has developed greatly in recent years as a result of advances in information technology. Financial time-series tasks have made substantial use of machine learning and deep neural networks, but building a prediction model from scratch takes time and computational resources. Transfer learning is growing popular in tackling these constraints of training time and computational resources in several disciplines. This study proposes a hybrid base model for the financial time series prediction employing the recurrent neural network (RNN) and long-short term memory (LSTM) called RNN-LSTM. We used random search to fine-tune the hyperparameters and compared our proposed model to the RNN and LSTM base models and evaluate using the RMSE, MAE, and MAPE metrics. When forecasting Forex currency pairs GBP/USD, USD/ZAR, and AUD/NZD our proposed base model for transfer learning outperforms RNN and LSTM base model with root mean squared errors of 0.007656, 0.165250, and 0.001730 respectively.

关键词： Deep Learning Transfer Learning Time Series Analysis RNN LSTM

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A Hybrid Spatial Dependence Model Based on Radial Basis Function Neural Networks (RBFNN) and Random Forest (RF)

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Journal of Data Analysis and Information Processing 2023年第3期11卷 293-309页

作者： Mamadou Hady Barry lawrence nderu Anthony Waititu Gichuhi Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Department of Computing School and Information Technology (SCIT) Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya Department of Statistics and Actuarial Science Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

The majority of spatial data reveal some degree of spatial dependence. The term “spatial dependence” refers to the tendency for phenomena to be more similar when they occur close together than when they occur far apart in space. This property is ignored in machine learning (ML) for spatial domains of application. Most classical machine learning algorithms are generally inappropriate unless modified in some way to account for it. In this study, we proposed an approach that aimed to improve a ML model to detect the dependence without incorporating any spatial features in the learning process. To detect this dependence while also improving performance, a hybrid model was used based on two representative algorithms. In addition, cross-validation method was used to make the model stable. Furthermore, global moran’s I and local moran were used to capture the spatial dependence in the residuals. The results show that the HM has significant with a R2 of 99.91% performance compared to RBFNN and RF that have 74.22% and 82.26% as R2 respectively. With lower errors, the HM was able to achieve an average test error of 0.033% and a positive global moran’s of 0.12. We concluded that as the R2 value increases, the models become weaker in terms of capturing the dependence.

关键词： Spatial Data Spatial Dependence Hybrid Model Machine Learning Algorithms

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