Browsing by Author "Hewaarachchi, A.P."

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Forecasting monthly household water consumption supplied by NWSD, Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Perera, M.L.D.M.; Hewaarachchi, A.P.
Water is an essential element for the survival of mankind and water supply is a pressing issue in this century. Household water use is generally the most important component of water consumption. In Sri Lanka, lack of freshwater has become a serious problem due to factors like population growth, overall expansion in economic activities, increased urbanization and changing climate patterns. Then as a country, managing water resources more efficiently has become a priority. This it is vital to forecast future monthly water consumption of households for planning purposes of further developments of the country. In this research, we aim to determine a suitable model for monthly household water consumption supplied by National Water Supply & Drainage Board (NWSDB), Sri Lanka in order to forecast future household water consumption. We consider monthly household water consumption data in Sri Lanka for the period from January 2005 to August 2016. The data shows an upward trend which suggests that the series is non-stationary. Also, data displays increasing variability and there’s a need to apply data transformation to stabilize the variance. Then, differencing techniques are applied to obtain a stationary series. Using Box-Jenkins methodology SARIMA (Seasonal Autoregressive Integrated Moving Average) model is identified as a reasonable model for the data. The result showed among several plausible ARIMA models, ARIMA (2, 1, 0) (1, 0, 1)12 model was appropriate for forecasting future values as it has the smallest AIC (Akaike information criterion) value. As a model validation technique, this model is then used to forecast last 5% of observations of data set. The accuracy of forecast error was assessed by mean percent error (MPE), mean absolute squared error (MASE) and mean absolute percent error (MAPE). The measures were 0.488, 0.287 and 2.213 respectively. As a future work it will be worthwhile to forecast water consumption for different regions. Also, to improve the accuracy of forecasts, models, which incorporate influential factors such as monthly precipitation, number of new connection will be considered.
Homogenization of Daily Temperature Data
(American Meteorological Society, 2017) Hewaarachchi, A.P.; Li, Yingbo; Lund, Robert; Rennie, Jared
This paper develops a method for homogenizing daily temperature series. While daily temperatures are statistically more complex than annual or monthly temperatures, techniques and computational methods have been accumulating that can now model and analyze all salient statistical characteristics of daily temperature series. The goal here is to combine these techniques in an efficient manner for multiple changepoint identification in daily series; computational speed is critical as a century of daily data has over 36 500 data points. The method developed here takes into account 1) metadata, 2) reference series, 3) seasonal cycles, and 4) autocorrelation. Autocorrelation is especially important: ignoring it can degrade changepoint techniques, and sample autocorrelations of day-to-day temperature anomalies are often as large as 0.7. While daily homogenization is not conducted as commonly as monthly or annual homogenization, daily analyses provide greater detection precision as they are roughly 30 times as long as monthly records. For example, it is relatively easy to detect two changepoints less than two years apart with daily data, but virtually impossible to flag these in corresponding annually averaged data. The developed methods are shown to work in simulation studies and applied in the analysis of 46 years of daily temperatures from South Haven, Michigan.
Multivariate time series models for temperature data in Nuwara Eliya, Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Wijayawardhana, H.N.A.M.; Hewaarachchi, A.P.
Sri Lanka is located north of the equator. Hence, its climate is regarded as a tropical climate and the average mean temperature in Sri Lanka is 27.0 °C (81 °F). However, the temperature in some parts of the island deviates from the typical temperature pattern. Especially the cities in the central parts of Sri Lanka are situated far above the sea level, and hence temperature in these regions is low and experience seasonal behaviour. Nuwara Eliya district is one of the tourism hotspots of Sri Lanka, which is located in the central province. The lowest annual regional temperature in Sri Lanka is recorded in weather stations at Nuwara Eliya, which is about 15.0 °C (59 °F). Nuwara Eliya district is long known for its high-quality agricultural products including tea, vegetables, and fruits. The behaviour of temperature heavily influences these two industries (tourism and agriculture). Therefore, it is very significant to analyze the temperature in Nuwara Eliya. Thus, in this study, we expect to analyze the monthly atmospheric temperature of Nuwara Eliya. For that, monthly maximum, and minimum temperature series in Nuwara Eliya from 1997 to 2018 were collected from the Department of Meteorology, Sri Lanka. This study focused on seeking the joint behaviour of monthly maximum and minimum temperature series while analyzing the correlation structure of both series. Using regression analysis, the seasonal components and trend components of both series were estimated. However, according to the trend analysis, both series did not experience a significant trend during the considered time period. Then, using the whitening technique, a significant cross-correlation between the seasonally adjusted two series was investigated. A VAR (Vector Autoregression) model was fitted to represent the joint behaviour of the two deseasonalized temperature series. VAR (3) model was selected as the best multivariate model for the two series. In addition, the forecasting accuracy using the multivariate model was assessed. The resulted mean absolute percentage error values (MAPE) are 6.29% and 2.43% for minimum and maximum series respectively. These MAPE values confirm that the model can be utilized for better predictions with higher accuracy.
A Statistical Analysis of Daily Snow Depth Trends in North America
(Atmosphere, 2021) Woody, J.; Xu, Y.; Dyer, J.; Lund, R; Hewaarachchi, A.P.
Several attempts to assess regional snow depth trends have been previously made. These studies estimate trends by applying various statistical methods to snow depths, new snowfalls, or their climatological proxies such as snow water equivalents. In most of these studies, inhomogeneities (changepoints) were not accounted for in the analysis. Changepoint features can dramatically influence trend inferences from climate time series. The purpose of this paper is to present a detailed statistical methodology to estimate trends of a time series of daily snow depths that account for changepoint features. The methods are illustrated in the analysis of a daily snow depth data set from North America.
A Time Series Analysis to Forecast Monthly Producer Price Indices of Manufacturing Sector and Textile Manufacturing Subcategory of Sri Lanka
(Faculty of Commerce and Management Studies, University of Kelaniya, 2021) Weerakoon, H.L.A.; Jayawardhana, K.J.U.M.; Piyasena, K.N.T.; Hewaarachchi, A.P.
The producer price index (PPI) is a group of indices that represents the average movement in selling prices from domestic production over time. PPI covers major sectors of a country’s economy, and it is used as an objective tool for adjusting prices in long-term purchasing agreements. A selected number of bivariate analyses have been done with consumer price indices. The purpose of this study was to fit a suitable bivariate time series model to forecast the manufacturing PPI of Sri Lanka using the PPIs of the textile subcategory with the use of 81 observations from January 2014 to September 2020. The preliminary analysis identified a strong positive correlation of 0.9127 among the series. Further, ensuring stationarity with differencing (I(2)) and analyzing the cross-correlation plots of the prewhitened time series, a vector auto regression of order 8 model was fitted with lag selection based on minimum Akaike– information criterion (AIC). The model indicated significant coefficients with an Rsquared value of 0.6837, claiming that almost 68% of the manufacturing PPI can be forecasted with past values of textile manufacturing PPI adhering to assumptions of residual diagnostics except for the normality. The results of the Granger causality test revealed one-sided causality among the two original series but the VAR (8) model failed to indicate causality among the two I(2) series. Further, the presence of cointegration confirmed with the Johansen cointegration test revealed long-run equilibrium. Hence a vector error correction model (VECM) was fitted which adhered to assumptions of model residuals including serial correlation, heteroscedasticity, and normality.
Time series models to forecast number of registered cars in Sri Lanka
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Sanjeewani, D.S.; Hewaarachchi, A.P.; Gunaratne, M.D.N.
Even though Sri Lanka is a developing country, the automobile industry of Sri Lanka displays rapid growth recently. The Department of Motor Traffic reports indicates that the number of vehicle registrations has been steadily increasing. Therefore, the analysis of car registrations is crucial for the economic development and legislation process of Sri Lanka. This study aims to predict the total number of registered cars and to predict the number of registered cars by categories i.e. brand new, reconditioned and locally manufactured. Time series analysis enables forecasting the number of registered cars based on the past car registration patterns and comparing them with current trends. This study uses the number of registered reconditioned, brand-new, and local cars from January 2008 to December 2018 and attempts to create better forecasts using BoxJenkins time series models and Holt's double exponential smoothing technique. Furthermore, this study plan to test if one level, the bottom-up approach of hierarchical forecasting (number of total registered cars as the sum of registered reconditioned, brand-new and local cars) outperforms forecasting number of registered total cars as a whole. Firstly, 90% of the data was considered as the training set for the analysis, and the remaining 10% of the data was considered as the testing set. To accurately model the high volatility of data, generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models were used. The Moving Average (MA)(1)+GARCH(1,1) model was fitted to predict reconditioned car registration data. The Autoregressive Integrated Moving Average (ARIMA)(4,1,7)+EARCH(1,1) model was fitted to brand-new car registration data. The ARIMA(1,1,7)+GARCH(1,1) model was fitted to analyze local car registrations over time. Finally, MA(1)+ autoregressive conditional heteroscedasticity (ARCH)(1) model was fitted to extrapolate total car registration data. The mean absolute percentage error (MAPE) was used as the accuracy measure since it does not depend on the scale. Hence, the model which gives the minimum MAPE was selected to forecast the number of registered cars. The fitted models indicate satisfactory forecast results, which are 13% in MAPE for reconditioned car registrations using Holt's double exponential technique, 18% in MAPE for brand new car registrations using ARIMA(4,1,7) + EGARCH(1,1) hybrid model, and 19% in MAPE for local car registrations using Holt's double exponential technique. The total number of car registration is predicted using the one level hierarchical forecasting technique with 13% in MAPE. Concerning the findings, the number of registered cars highly fluctuated over the period. Besides, ARIMA is not adequate to capture the volatility, and the hierarchical forecasting technique is better to forecast total car registration data other than forecast it as a whole. This study predicted the number of registered cars considering different car categories and comparing time series models with smoothing techniques. Moreover, novel methods such as EGARCH and hierarchical approach were used to make more accurate predictions. This study's major limitation is irregular variations due to the influence of external factors that could be addressed in future research.