Hierarchical aggregation/disaggregation of time series in order to make forecasts is a frequent challenge in business and econometric scenarios. This work presents a novel approach for selecting an adequate time series disaggregation level as a starting point for making forecasts. The...

This study develops a framework to forecast India's gross domestic product growth on a quarterly frequency from 2004 to 2018. The models, which are based on real and monetary sector descriptions of the Indian economy, are estimated using Bayesian vector autoregression (BVAR) techniques. The real...

The behavior of agricultural commodity markets can arguably result in markedly asymmetricprice cycles, that is, downward cycles of substantially different length and breadth thanupward cycles. This study assesses whether asymmetric-cycle models can enhance the understandingof the dynamics and...

The problem of forecasting time series is very widely debated. In recent years, machine learning algorithms have been very prolific in this area. This paper describes a systematic approach to building a machine learning predictive model for solving optimization problems in the banking sector. A...

Improving the accuracy of cash flow forecasting in the TSA is the key to fulfilling government payment obligations, minimizing the cost of maintaining the cash reserve, providing the absence of outstanding debt accumulation, and ensuring investment in various financial instruments to obtain...

The forecasting of prices for electricity balancing reserve power can essentially improve the trading positions of market participants in competitive auctions. Having identified a lack of literature related to forecasting balancing reserve prices, we deploy approaches originating from...