Solar flares are among the most intense solar phenomena, and precise forecasting of solar flares holds significant importance in space weather research. The ability to predict solar flares is crucial for protecting the near-Earth space environment and preserving the integrity of our technological infrastructure from potential detrimental consequences. Over the last two decades, machine learning and deep learning models have made a significant impact on the prediction of solar flares, leveraging their capacity to learn from high-dimensional data spaces. However, the scarcity of high-quality data from the field of solar flare prediction becomes a daunting challenge for such tasks. One of the methods to tackle the scarcity of high-quality data is to generate synthetic samples (i.e., data augmentation). In this study, we aim to explore the role of data augmentation on time series-based flare prediction models, namely, deep learning-based methods. We utilize the latest time series-based benchmark dataset extracted from the vector magnetograms of Solar Dynamics Observatory’s Helioseismic and Magnetic Imager (SDO/HMI). Specifically, we use seven-time series data augmentation techniques to enrich our dataset and train three machine learning models for multivariate time series classification. To our knowledge, this is the first research effort that attempts to explore data augmentation’s impact on the solar flare prediction problem.