Energy Consumption Synthesis through Time-Series Image Representation: A GAN-Based Approach - Kalliala, Karri Hermanni (2024)
Time-series forecasting is a longstanding problem, continually evolving with new methodologies. A significant portion of today's data consists of timestamped measurements, such as stock prices, medical monitoring, application logs, weather records, and energy consumption data. Most deep-learning approaches for forecasting time-series data rely on the memory of processing cells, which remember past events and connect them to future occurrences. This thesis explores an innovative method by transforming time-series datasets into the format of colored digital images and employing Convolutional Neural Networks (CNNs) to process multiple cross-correlating time-series datasets simultaneously. This method allows for the analysis of the same time of day across multiple days using a single convolution kernel.