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Machine Learning (ML) plays a critical role in decoding these time-series data, predicting future trends, monitoring systems, diagnosing issues, and optimizing performance across various sectors. \nHowever, the complexities associated with time-varying data, wherein not only the sensor measurements but also the number of sensors and their underlying patterns can change over time, make it a demanding task to derive useful predictions through ML.\n\nThis thesis is a deep dive into ML and transfer learning techniques, specifically applied to time-varying data. We tackled three significant challenges in the field: imbalances in time-series regression, increasing input dimensions, and covariate shift. To each challenge, we provided innovative solutions, rigorously testing them in real-world scenarios. 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We introduced a transfer learning method to add new inputs to an existing prediction task, separating historical data and new data into source and target datasets. This approach, theoretically sound and robust against negative transfer learning, demonstrated superior performance on multiple real-life datasets. It proves easy to implement and can handle tasks even when the new input data is scarce, making it a valuable addition to the field.\n\nThe third challenge we tackled was the covariate shift, which originates when input data changes its distribution over time. Our research led to the creation of a transfer learning method using the minimum error entropy (MEE) criterion as a loss function, known for its robustness against various noise types. 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To address these shortcomings, this paper proposes a new ensemble machine-learning model for software defect prediction using k Nearest Neighbour (kNN), Generalized Linear Model with Elastic Net Regularization (GLMNet), and Linear Discriminant Analysis (LDA) with Random Forest as base learner. Experiments were conducted using the proposed model on CM1, JM1, KC3, and PC3 datasets from the NASA PROMISE repository using the RStudio simulation tool. The ensemble technique achieved 87.69% for CM1 dataset, 81.11% for JM1 dataset, 90.70% for PC3 dataset, and 94.74% for KC3 dataset. The performance of the proposed system was compared with that of other existing techniques in literature in terms of AUC. The ensemble technique achieved 87%, which is better than the other seven state-of-the-art techniques under consideration. On average, the proposed model achieved an overall prediction accuracy of 88.56% for all datasets used for experiments. 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