Auxiliary-lag Dependent Gaussian Process Model for Forecasting Using Proposed Kernels and Multi-start Optimization Method

Pakistan is currently affected by climate change and facing floods due to monsoon rains. Also, it impacts agriculture production, and being an agricultural land, it has a significant role in the economy of Pakistan. Rainfall and agriculture production forecasting are very important for policy making. In this paper, we have presented an auxiliary-lag dependent Gaussian process, a Bayesian non-parametric machine learning model, for forecasting using auxiliary lags. We have also introduced some new multifeatured kernel functions that are versatile in dealing with seasonal data. For comparison of the proposed model, we have used the autoregressive random forest model, autoregressive artificial neural network model, seasonal autoregressive moving average models, and exponential smoothing models. Results confirmed the superiority of the proposed model over conventional models. The proposed methodology will be helpful for other researchers and local experts in making more reliable forecasting which will be helpful in policymaking relevant to agriculture systems, water management systems, climate change, and natural disasters such as droughts and floods.