in India. The order of the best ARIMA model was found to be (2,1,0). Further, efforts were made to forecast, as accurate …A time series modeling approach (Box-Jenkins’ ARIMA model) has been used in this study to forecast sugarcane production … as possible, the future sugarcane production for a period upto five years by fitting ARIMA(2,1,0) model to our time …

The forecasting of energy consumption is essential for any country to study the future energy demand and to introduce … the necessary government policies. This paper presents the formulation of forecasting models based on the Artificial … Neural Network (ANN) for the consumption of conventional energy sources. In India, the total energy consumption for coal, oil …

The forecasting of energy consumption is essential for any country to study the future energy demand and to introduce … the necessary government policies. This paper presents the formulation of forecasting models based on the Artificial … Neural Network (ANN) for the consumption of conventional energy sources. In India, the total energy consumption for coal, oil …

The rapid growth in banking indicators in the North Eastern region of India following nationalisation of 14 major banks …

forecasting. The parameter space for SSOE models may be specified to match that of the corresponding ARIMA scheme, or it may be …The state space approach to modelling univariate time series is now widely used both in theory and in applications … many similarities between the MSOE and SSOE schemes, but also some important differences. Both have ARIMA models as their …

In this article, we examine the daily water demand forecasting performance of double seasonal univariate time series … models (Holt-Winters, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. A within-week seasonal cycle …

DSE. Two forecasting techniques, Autoregressive Integrated Moving Average (ARIMA) and neural network, are used to test the … market in semi-strong form. Besides, the DSE market index is not random and the trend could be captured by ARIMA and neural …

Forecasting exchange rate movements is challenging, as they exhibit high volatility, complexity and noise. Most … Autoregressive Integrated Moving Average (ARIMA) model. Performance of both models is rigorously evaluated using three different … for different sampling periods. Empirical results show that ANN per-forms better than ARIMA and delivered consistent …

presents the analytical forecasting properties of the random coefficient exponential smoothing model in the multiple source of … reduced form ARIMA parameters. In addition, it shows that parametric mapping surmounts the difficulties that are likely to … comparing the forecast accuracy of the suggested model vis-�-vis other benchmark models, both in the ARIMA and in the …