Forecasting Ozone Density in Tehran Air Using a Smart Data-Driven Approach

A Conditional Probability Density Function for Forecasting Ozone Air Quality Data

Probabilistic forecasts are often employed to estimate the potential for high pollutant concentrations. To develop a probabilistic forecast of ozone concentrations, we suggest that use be made of the inherent properties of seasonality and autocorrelation in 0, time series. A non-stationary, autocorrelated stochastic process is used to simulate a conditional probability density function (p.d.f.)...

Forecasting Air Pollution Concentrations in Iran, Using a Hybrid Model

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

Forecasting Of Tehran Stock Exchange Index by Using Data Mining Approach Based on Artificial Intelligence Algorithms

Uncertainty in the capital market means the difference between the expected values ​​and the amounts that actually occur. Designing different analytical and forecasting methods in the capital market is also less likely due to the high amount of this and the need to know future prices with greater certainty or uncertainty. In order to capitalize on the capital market, investors have always sough...

Forecasting Air Pollution Concentrations in Iran, Using a Hybrid Model

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

Local Real-time Forecasting of Ozone Exposure using Temperature Data

Local Real-time Forecasting of Ozone Exposure using Temperature Data

Rigorous and prompt assessment of ambient ozone exposure is important for informing the public about ozone levels that may lead to adverse health effects. In this paper, we make use of hierarchical modeling to forecast 8-hour average ozone exposure. Our contribution is to show how incorporating temperature data in addition to observed ozone can significantly improve forecast accuracy, as measur...