Developing a Standard Strategy for Time Series Forecasting Integrating Statistical and Machine Learning Techniques Using a Meta-Model Approach and its Application in Generating External Debt Projections

The pursuit of accurate forecasting and the rise of machine learning have led to the development of various forecasting models, making model selection increasingly difficult. This paper aims to address this challenge by developing a standard strategy for time series forecasting using a meta-model approach. A meta-model is constructed by combining individual forecasts from leading statistical and machine learning models, with Philippine external debt as the target variable. The baseline meta-model combines the following individual forecasts using ordinary least squares (OLS): (a) random walk with drift, (b) autoregressive integrated moving average (ARIMA), (c) exponential smoothing with error, trend, and seasonal components (ETS), (d) Holt-Winters, (e) multiple aggregation prediction algorithm (MAPA), (f) temporal hierarchical forecasting (THieF), (g) theta model, (h) Prophet, (i) neural network autoregression (NNAR), (j) long short-term memory (LSTM), (k) gradient boosting machine (GBM), (l) extreme gradient boosting (XGBoost), (m) random forest, (n) support vector regression (SVR), and (o) dynamic linear models (DLM). Alternative meta-models were also evaluated. The best-performing meta-model, which employs the Least Absolute Shrinkage and Selection Operator (LASSO), performs remarkably well, achieving a mean absolute percentage error (MAPE) of 3.0 percent in the validation set. The proposed standard strategy is versatile, with the potential to forecast other financial and economic variables. Additionally, forecasts generated by the meta-model can serve as a valuable benchmark, whether compared to current forecasting practices or in cases where no forecasting methodology exists.