Paper Title
Progress and Challenges Toward AI-Powered Time Series Forecasting

Abstract
This research investigates the progress and challenges ofartificial intelligence (AI) powered time series forecasting models by focusing on model accuracy and validation with an example of a gasoline price forecasting model. Since this topic is so new and details are still emerging, in this research we first try toanalyze the data using a traditional forecasting model development procedure using R. We then explore the progress and challengesif AI-powered forecasting model approach were used. Since time series data is sequential, vastly different in nature, and often influenced by sentiment and government policyas opposed to these of large language models (LLMs) that are widely used for words and pixels, the main research is: Can AI-powered time series forecasting not only facilitate the automation of the model development process, but also enhance the model accuracy and validation?Moreover, since time series data is less readily available in terms of public datasets as opposed to that of LLMs and since the sequential order of a time series data has to be strictly preserved, what are the unique challenges? Since time series data is not necessarily independent and identically distributedin general, one of the major challenges is how toguarantee that the future performance will repeat in the same pattern as in the historical data regardless how well the forecasting model fits the training data. Keywords - Time Series Forecast, Gasoline Price Forecast, AI-Powered Forecasting, Model Validation and Evaluation, Challenges, Cross-Validation