Trade-off Clustering Approach for Multivariate Multi-Step Ahead Time-Series Forecasting

Abstract

Time-Series forecasting has gained a lot of steam in recent years. With the advent of Big Data, a considerable amount of data is more available across multiple fields, thus providing an opportunity for processing historical business-oriented data in an attempt to predict trends, identify changes and inform strategic decision-making. The abundance of time-series data has prompted the development of state-of-the-art machine learning algorithms, such as neural networks, capable of forecasting both univariate and multivariate time-series data. Various time-series forecasting approaches can be implemented when leveraging the potential of deep neural networks. Determining the upsides and downsides of each approach when presented with univariate or multivariate time-series data, thus becomes a crucial matter. This evaluation focuses on three forecasting approaches: a single model forecasting approach (SMFA), a global model forecasting model (GMFA) and a cluster-based forecasting approach (CBFA). The study highlights the fact that the decision pertaining to the finest forecasting approach often is a question of trade-off between accuracy, execution time and dataset size. In this study, we also compare the performance of 6 deep learning architectures when dealing with both univariate and multivariate time-series datasets for multi-step ahead time-series forecasting, across 6 benchmark datasets.