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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.

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The methods used for forecasting financial series are based on the concept that a pattern can be identified in the data and distinguished from randomness by smoothing past values. This smoothing process eliminates randomness from the data, enabling the inherent pattern to be used for forecasting. However, acquiring high frequency national accounts data can be challenging, and complicated methods are required to achieve disaggregated series that are compatible with annual totals. Therefore, there is a need for simpler techniques to obtain high frequency data from low frequency equivalents. ML algorithms are rapidly evolving, and feed forward ANNs with appropriate training mechanisms have been proposed to temporally disaggregate economic series without considering related indicators. This study proposes using the ANNs algorithm to disaggregate national statistical accounts. An application of disaggregating annual Australia GDP data into quarterly data has also been presented. The higher frequency data generated has been compared with the observed quarterly data to assess its accuracy. Comparative study suggests that the ANN-based model outperforms over benchmark methods such as Chow and Lin method (cl1) and Fernandez method (f).