Incremental Learning Vector Auto Regression for Forecasting with Edge Devices

It is common to forecast time-series data in a cloud server environment by building a forecasting model after collecting all the time-series data at the server-side. However, this may not be efficient in time-critical forecasting, control, and decision-making due to high latency, bandwidth, and network connectivity issues. Hence, edge devices can be employed to make quick forecasting on a real-time basis. However, due to limited computing resources and processing power, edge devices cannot handle a huge volume of multivariate time-series data. Therefore, it is desirable to develop an algorithm that trains and updates a forecasting model incrementally. This can be done by using a small chunk of multivariate time-series data without sacrificing the forecasting accuracy, while training and inference can be executed in the edge device itself. In this context, we propose a new forecasting method called Incremental Learning Vector Auto Regression (ILVAR). It works by minimizing the variance difference between actual and forecasted values as a new chunk of time-series data arrives sequentially and thereby it updates the forecasting model incrementally. To show the effectiveness of the proposed method, experiments were performed on 11 publicly available datasets from diverse domains using Raspberry Pi-2 as an edge device and evaluated using five metrics such as MAPE, RMSE, $\mathrm{R}^{2}$ score, Computational time, and Memory consumption for 1-step and 24-step ahead forecasting tasks. The performance was compared with the state-of-the-art methods such as Vector Auto Regression (VAR), Incremental Learning Extreme Learning Machine (ILELM), and Incremental Learning Long Short-Term Memory (ILLSTM). These experimental results suggest that our proposed method performs better than existing methods and is able to achieve the desired performance for forecasting with edge devices.