The well-known Box-Jenkins’ Autoregressive Integrated Moving Average (ARIMA) methodology for fitting time-series data has some major limitations. To this end, Exponential Autoregressive (EXPAR) family of models may be employed. An important characteristic feature of EXPAR is that it is capable of modelling those data sets that depict cyclical variations. Further, it can also be used when data s...

In this paper, we present a novel approach to estimate traffic speed using a sequence of images from an uncalibrated camera. We assert that exact calibration is not necessary to estimate speed. Instead, we use 1) geometric relationships inherently available in the image, 2) some common-sense assumptions that reduce the problem to a one-dimensional (1-D) geometry, 3) frame differencing to isolat...

To investigate the applicability of ARIMA models in wholesale vegetable market models are built taking sales data of one perishable vegetable from Ahmedabad wholesales market in India. It is found that these models can be applied to forecast the demand with Mean Absolute Percentage Error (MAPE) in the range of 30%. This error is acceptable in fresh produce market where the demand and prices are...

This paper proposes a technique to implement wavelet analysis (WA) for improving a forecasting accuracy of the autoregressive integrated moving average model (ARIMA) in nonlinear time-series. With the assumption of the linear correlation, and conventional seasonality adjustment methods used in ARIMA (that is, differencing, X11, and X12), the model might fail to capture any nonlinear pattern. Ra...

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...

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...

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 ...

The Hungarian mortality rates were analyzed by autoregressive integrated moving average models and seasonal time series models examined the data of acute childhood lymphoid leukaemia. The mortality data may be analyzed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality rates of cerebro...