GETS: grammatical evolution based optimization of smoothing parameters in univariate time series forecasting

Time series forecasting is a technique that predicts future values using time as one of the dimensions. The learning process is strongly controlled by fine-tuning of various hyperparameters which is often resource extensive and requires domain knowledge. This research work focuses on automatically evolving suitable hyperparameters of time series for level, trend and seasonality components using Grammatical Evolution. The proposed Grammatical Evolution Time Series framework can accept datasets from various domains and select the appropriate parameter values based on the nature of dataset. The forecasted results are compared with a traditional grid search algorithm on the basis of error metric, efficiency and scalability.