In an era of significant economic volatility, time series forecasting is widely used to predict stock prices and guide investors in trading decisions. Nevertheless, existing data-driven techniques are unable to effectively handle the vast amount of financial data due to big data constraints such as nonlinearity, non-stationarity, heteroskedasticity, and unsynchronicity. A cohesive framework is also required for ensuring the smooth integration and synchronization of varied methodologies in timeseries financial prediction tasks. To address this problem, this paper introduces a novel framework that investigates three ensemble strategies: blending, stacking, and voting, and selects the best method to perform the stock trend prediction task. Specifically, we deploy four distinct machine learning algorithms as the base learning model, each of which is uncorrelated and proficient in a different way depending on the task. The outputs of the basis classifiers are then combined using the adaptive boosting algorithm, a meta classifier, to give the final prediction results. To augment predictive models's accuracy and generalization capabilities, we put forward strategies like feature engineering and Ridge regularization, which optimize the pertinence of data and curb overfitting. Our examination of five distinct case studies on Toronto Stock Exchange data reveals that the proposed multimodel ensemble method has superior performance compared to others.

This study introduces a sophisticated supervised machine learning method for electric theft detection utilizing a customized Histogram Gradient Boosting (HGB) algorithm. Comprehensive preprocessing, including imputation, normalization, outlier management, and resampling, ensures the timeseries data is accurately prepared for analysis. The SMOTE-ENN algorithm corrects class imbalances, preparing the data for the feature optimization stage where crucial features are selected and extracted. The HGB algorithm, enhanced through Bayesian optimization, is central to the training process, resulting in a model that precisely classifies electricity consumption patterns as genuine or fraudulent. The robustness of the model is assessed against other recognized boosting methods, such as Adaptive Boosting (ADB), Gradient Boosting Decision Tree (GBDT), and LightGBM, alongside various ensemble and traditional machine learning models. Utilizing key performance metrics like accuracy, F1 score, and AUC for validation, the proposed model yields very promising results, with a 93% accuracy, 95% F1 score, and 98% AUC, outperforming the comparison group under similar dataset and hyperparameter conditions. This underscores the model's potential as a highly accurate tool for combating electricity theft within an advanced metering infrastructure (AMI).