abstract nowadays, due to the environmental uncertainty and rapid development of new technologies, economic variables are often predicted by using less data and short-term timeframes. therefore, prediction methods which require fewer amounts of data are needed. auto regressive integrated moving average (arima) model and artificial neural networks (anns) need large amounts of data to achieve acc...

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average w...

In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we div...

short-term traffic flow forecasting plays a significant role in the intelligent transportation systems (its), especially for the traffic signal control and the transportation planning research. two mainly problems restrict the forecasting of urban freeway traffic parameters. one is the freeway traffic changes non-regularly under the heterogeneous traffic conditions, and the other is the success...

In today’s world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting...

Flooding is the most common natural disaster and continues to increase in frequency intensity due climate changes [7]. Currently, there a lack of efficient tools predict flooding. This research aimed create Time Series Machine Learning (ML) program using Auto Regressive Moving Average (ARIMA) models forecast streamflow, one prominent factors flood prediction. A streamflow dataset from Ganges Ri...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...