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...

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...

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 ...

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 ...

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...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...