Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models.

A novel method for rainfall forecasting has been proposed using Multi Resolution Analysis (MRA). This approach decomposes annual rainfall series and long-term climate indices into component sub-series at different temporal scales, allowing for a more detailed analysis of the factors influencing annual rainfall. Multiple Linear Regression (MLR) is then used to predict annual rainfall, with climate indices sub-series as predictive variables, using a step-wise linear regression algorithm. The proposed model has been tested on Indian annual rainfall data and compared with the traditional MLR model. Results show that the MRA-based model outperforms the traditional model in terms of relative absolute error and correlation coefficient metrics. The proposed method offers several advantages over traditional methods as it can identify underlying factors affecting annual rainfall at different temporal scales, providing more accurate and reliable rainfall forecasts for better water resource management and agricultural planning. In conclusion, the MRA-based approach is a promising tool for improving the accuracy of annual rainfall predictions, and its implementation can lead to better water resource management and agricultural planning.

Seasonal production, weather abnormalities, and high perishability introduce a high degree of volatility to potato prices. Price volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it in a dissimilar way. GARCH is a symmetric model, and it cannot capture asymmetric price volatility. EGARCH, APARCH, and GJR-GARCH models are popularly used to capture asymmetric price volatility. In this paper, an attempt is made to model the price volatility of the weekly wholesale modal price of potatoes for the Agra, Ahmedabad, Bengaluru, Delhi, Kolkata, and Mumbai markets using the above-mentioned models. The News Impact Curves (NICs) are derived from the fitted models, which confirmed the presence of asymmetry in the price volatility. To this end, NICs are used to describe the degree of asymmetry in volatility present in different markets.