Time series forecasting is the process of analyzing historical data in order to draw conclusions and predict future outcomes. For this project, we explore, compare, and contrast different methods and models of forecasting to determine the advantages and disadvantages of these different systems. We explore, through research papers and code documentation, the commonly used existing methods for working with time series data and present the methods that will be focused on. At regular intervals throughout this project, we will provide presentations covering the results of the different models and the takeaways from working with each method.
The models used for this project have been found from research into what methods are most commonly used for time series forecasting. These models have included ARIMA, Exponential Smoothing, Theta, TBATS, Regression, Recurrent Neural Networks, the Temporal Fusion Transformer, and others. These models were tested on multiple types of datasets, including simple univariate and multivariate datasets tracking sales of different products, as well as bigger and more complex datasets, such as one tracking energy usage in a household, along with covariates of weather and temperature. Below are the results of the Temporal Fusion Transformer on this dataset, with the Root Mean Square Error given at the top.
Included below are a full paper and presentation detailing the topic and the achieved results. More work will be done in the future to implement these methods into other projects.