Probabilistic Time Series Forecasting using Bayesian and Non-Bayesian Methods with Applications

Forecasting plays a crucial role in informed decision-making across a wide range of domains. Inspired by different application settings, this thesis addresses real-world forecasting problems and incorporates the findings into three novel models. They are developed under both Bayesian and non-Bayesian frameworks, and contribute significant advancements to the field of time series forecasting.