Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

/pdf/forecasting-daily-meteorological-time-series-using-arima-and-2kihsgmlys.pdf

Forecasting daily meteorological time series using ARIMA and regression models

Impact of inflation rate on construction projects budget: A review

(2007). Essentials of Statistics for Business and Economics. The American Statistician: Vol. 61, No. 2, pp. 182-183.

Essentials of Statistics for Business and Economics

In spite of the advancement in the management of the construction industry, construction projects still facing the issue of cost overrun. Many factors influence the cost overrun but one of the most critical is the inflation rate, which plays a vital role in deviating prices of construction materials during the project lifecycle. Therefore, this study aims to investigate the behaviour of building materials prices and their correlation with the inflation rate. The Spearman test was adopted to measure the correlation between the building materials prices and the inflation rate. It was revealed that a significant positive and negative relationships between the inflation rate and the building materials prices exist, indicating that the change in the inflation rate will also affect the prices which will result in deviating the initial budget of a project. The correlation was investigated by the individual materials of cement, steel reinforcement, plumbing works, sanitary fitting, ironmongery, bricks, roofing, ceiling board, paints and plywood. Every materials group is representing an individual industry thus it can be concluded that the inflation rate is influencing these industries as well. Therefore, it is essential to consider the inflation rate while calculating the budget at the beginning of the construction projects, with a recommendation to develop a budget estimation model which can incorporate the changing behaviour of the building materials prices due to the inflation rate along with other essential parameters in setting up the project budget.

Investigating the impact of inflation on building materials prices in construction industry

Spatial forecasting of solar radiation using ARIMA model

Time series analysis of daily Nigerian Naira-US Dollar Exchange Rates (DNDER) data is conducted. The time plot reveals a positive trend. Seasonality of order 7 is observed; troughs tend to appear on Mondays and peaks on Fridays. Seasonal differencing once produced a series SDDNDER with a slightly overall negative trend. A non-seasonal differencing of SDDNDER yielded a series DSDDNDER with no trend but with a correlogram revealing seasonality of order 7. Moreover, the correlogram reveals the involvement of a seasonal moving average component of order 1 and a nonseasonal autoregressive component of order 2. An adequate multiplicative seasonal autoregressive integrated moving average (ARIMA) model, (2, 1, 0)x(0, 1, 1)7, is therefore fitted to the series.

http://www.aessweb.com/pdf-files/219-227.pdf

A Seasonal Arima Model for Daily Nigerian Naira-Us Dollar Exchange Rates

Brief review of literature of the well documented seasonal Box-Jenkins modelling is done. Rainfall is a seasonal phenomenon the world over. For illustrative purposes, monthly rainfall as measured in Port Harcourt, Nigeria, is modelled by a (5, 1, 0)x(0, 1, 1)12 seasonal ARIMA model. The time-plot shows no noticeable trend. The known and expected seasonality is clear from the plot. Seasonal (i.e. 12-point) differencing of the data is done, then a nonseasonal differencing is done of the seasonal differences. The correlogam of the resultant series reveals the expected 12-monthly seasonality, and the involvement of a seasonal moving average component in the first place and a nonseasonal autoregressive component of order 5. Hence the model mentioned above. The adequacy of the modelled has been established.

Modelling Monthly Rainfall Data of Port Harcourt, Nigeria by Seasonal Box-Jenkins Methods

The time series being rainfall data is a typical seasonal series of one-year period. The time-plot of the realization herein called GASR and its correlogram are as expected, reflecting seasonality of period 12. For instance, the autocorrelation function is oscillatory of period 12. A 12-point differencing yields a series called SDGASR with a generally horizontal secular trend. It is adjudged stationary by the Augmented Dickey Fuller unit root test. Its correlogram gives an indication of stationarity as well as an involvement of the presence of a seasonal moving average component of order one and a seasonal autoregressive component of order two. This autocorrelation structure suggests three multiplicative SARIMA models, namely: (0, 0, 0)x(0, 1, 1)12 , (0, 0, 1)x(0, 1, 1)12 and (0, 0, 1)x(2, 1, 1)12. The first model is adjudged the most adequate. Its residuals have been observed to be uncorrelated. It may be the basis for the forecasting of rain in the region for planning purposes.

http://www.ijsrpub.com/uploads/papers/IJSRK/IJSRK-2014/Jul/IJSRK-14-63.pdf

Time Series Analysis of Monthly Rainfall data for the Gadaref rainfall station, Sudan, by Sarima Methods

Time series analysis and forecasting has become a major tool in different applications in hydrology and environmental management fields. Linear stochastic models known as multiplicative seasonal autoregressive integrated moving average (SARIMA) model were used to simulate and forecast monthly streamflow of Rahad River, Sudan. For the analysis, monthly streamflow data for the years 1972 to 2009 were used. A visual inspection of the time plot gives the expected impression of a generally horizontal trend and 12-month seasonal periodicity. The seasonality observed in auto correlation function (ACF) and partial auto correlation function (PACF) plots of monthly streamflow data was removed using first order seasonal differencing prior to the development of the SARIMA model. Interestingly, the SARIMA (2, 0, 0) × (0, 1, 1)12 model developed was found to be most suitable for simulating monthly streamflow for Rahad River. The model was found appropriate to forecast three years of monthly streamflow and assist decision makers to establish priorities for water demand.

Application of linear stochastic models to monthly streamflow data of Rahad River, Sudan

Time series analysis of Nigerian Unemployment Rates is done. The data used is monthly from 1948 to 2008. The time plot reveals a slightly positive trend with no clear seasonality. A multiplicative seasonal model is suggestive given seasonality that typically tends to increase with time. Seasonal differencing once produced a series with no trend nor discernible stationarity. A non-seasonal differencing of the seasonal differences yielded a series with no trend but with a correlogram revealing stationarity of order 12, a nonseasonal autoregressive component of order 3 and a seasonal moving average component of order 1. A multiplicative seasonal autoregressive integrated moving average (ARIMA) model, (3, 1, 0)x(0, 1, 1)12, is fitted to the series. It has been shown to be adequate.

/pdf/a-multiplicative-seasonal-arima-model-for-nigerian-tbg1amlnl0.pdf

Ette Harrison Etuk

Papers

A Seasonal Arima Model for Daily Nigerian Naira-Us Dollar Exchange Rates

Modelling Monthly Rainfall Data of Port Harcourt, Nigeria by Seasonal Box-Jenkins Methods

Time Series Analysis of Monthly Rainfall data for the Gadaref rainfall station, Sudan, by Sarima Methods

Application of linear stochastic models to monthly streamflow data of Rahad River, Sudan

A Multiplicative Seasonal Arima Model for Nigerian Unemployment Rates