Time series forecasting and imputation is an important area of machine learning. Our project explores the use of convolutional style deep-learning methods to solve time series problems. Implementing these systems involves comparing accuracy metrics between other state of the art methods. Our team worked on developing and testing models for performing forecasting and imputation on air-quality specific data. We hope that by solving these problems we can more accurately predict and impute time-series data.