When addressing 'Garbage Collection' issues in Java-based Hadoop applications, adjusting the Heap size parameter is a key strategy. Garbage Collection is the process of automatically reclaiming memory occupied by objects that are no longer in use, and adjusting the Heap size helps optimize memory management in Hadoop applications.

The advantage of using PySpark is simplified development. Python is known for its simplicity and readability, making it easier for developers to write and maintain code, resulting in increased productivity in comparison to the complexities of conventional MapReduce jobs.

MRUnit is most commonly used for Unit Testing in the Hadoop ecosystem. It provides a framework for writing and running unit tests for MapReduce jobs, allowing developers to validate the correctness of their code in a controlled environment.

Checkpointing is essential for maintaining data consistency and reliability in distributed Hadoop data pipelines. It involves creating periodic checkpoints to save the current state of the application, enabling recovery from failures without reprocessing the entire dataset.

Apache Spark employs in-memory processing for efficient data transformation. It keeps intermediate data in memory, reducing the need to write to disk and significantly speeding up processing compared to traditional batch processing.

Data locality in MapReduce is achieved through node proximity. The framework schedules tasks to nodes where the data is already stored, minimizing data transfer over the network. This strategy enhances performance by reducing data movement and leveraging the proximity of computation and data.

When processing time-sensitive data, increasing Speculative Execution in the job configuration can help achieve timely execution. Speculative Execution involves running duplicate tasks on other nodes to finish faster, reducing the impact of slow-running tasks on job completion time.

Write-Ahead Logging (WAL) in Hadoop ensures data integrity during massive data ingestion. It records changes before they are applied, allowing recovery in case of system failure during the ingestion process.

In the context of periodic data analysis jobs, the Oozie component ensuring timely execution is the Bundle. Bundles provide a higher-level abstraction for managing and scheduling multiple coordinators. They allow you to group and coordinate multiple jobs, making them suitable for use cases involving periodic and interdependent data analysis tasks.

For historical data analysis and trend prediction, MapReduce is a suitable processing method. MapReduce efficiently processes large volumes of data in batch mode, making it well-suited for analyzing historical datasets and deriving insights for trend prediction.