Browsing by Author "Ekanayake, J.B."

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    An Intelligent Predicting Approach Based Long Short-Term Memory Model Using Numerical and Textual Data: The Case of Colombo Stock Exchange
    (Uva Wellassa University of Sri Lanka, 2019-02) Kumarasinghe, H.N.; Moneravilla, D.M.A.B.; Muwanwella, I.B.M.R.K.P.; Ekanayake, J.B.
    The data forecasting provides a significant guidance for making decisions in many areas especially in stock market today. Due to extremely dynamic and complicated nature of stock markets, price prediction has become a cumbrous challenge. However, there are certain underlying determinants which have a strong influence on the stock market. There are experiments from various areas aiming to take on that challenge and Machine Learning have been the focus of many of them. Nevertheless, many studies used either numerical or textual information, but not both for a single approach. In the present study, a forecasting model was developed to predict the stock prices based on the historical data, investor’s activities, macroeconomic variables and news articles. In the process of developing the model, number of factors influencing on stock prices were examined using the ordinary least squares method and technical indicators were identified by reviewing literature. The latest stock data and investor’s activities were collected from data library, issued by Colombo Stock Exchange on daily basis for a period of seven years from 2011. Interest rates, exchange rates were used as macroeconomic variables, which were collected from the reports of Central Bank of Sri Lanka. News articles were extracted using a sentimental analysis by analyzing news extracts from most popular news websites. Finally, the prediction model was developed based on recurrent neural network (RNN) and multivariate Long Short-Term Memory (LSTM) approach to predict stock market. The performance of the proposed approach is demonstrated on real-world data of 12 companies listed on Colombo Stock Exchange. The prediction quality of the models is evaluated using MAE, MPE, MAPE, MSE and RMSE. The LSTM and recurrent neural network provided a decent accuracy. The project developed a multivariate prediction model by abolishing the limitation of underutilization of sentiments in price prediction.
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    Is Rainfall Prediction Model Tested in One-Time Point Sufficient?
    (Uva Wellassa University of Sri Lanka, 2020) Dananjali, K.T.; Ekanayake, J.B.; Karunaratne, A.S.; Kumara, B.T.G.S.
    Many rainfall predictions models have been proposed. The common methodology followed by those models is that the model is trained using the data before the target and tested the model in one or a few points and claimed that the model is generalized. However, this project shows that the above procedure is not sufficient to generalize a rainfall prediction model as in some target periods the models failed to achieve a decent prediction quality. The models such as Multilayer Perceptron (MLP), M5P, and Linear Regression-were trained from the weather data collected between the years 2002 and 2015 from the station located at Badulla, Sri Lanka. Initially, the target period was set in the last week of the dataset and the training period was one week before the target week. Then, the training period was extended by one week, until the maximum length of the training period reached, keeping the target fixed. Next, the target period was brought back one week and the same procedure was repeated resulting in 695 models. The prediction quality was measured using Mean Absolute Error (MAE) and represented in heat-maps. The heat-maps show that the prediction quality varies over time. The highest accuracy was given by the MLP so that the MAE has fallen between 0 and 10 mm in 61.7% of the total instances. This indicates that testing models in one or a few time points are not sufficient for the generalization. Further, the reasons for such drastic changes in prediction quality will be investigated in our future projects. Keywords: Linear regression, M5P, Prediction, Multilayer perceptron, Rainfall
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    K-Means Clustering Algorithm to Predict the Badulla Tomato Price Based on Weather Factors
    (Uva Wellassa University of Sri Lanka, 2019-02) Dananjali, K.T.; Ekanayake, J.B.; Karunaratne, A.S.
    Tomato is one of the most important cash crops in Sri Lanka and tomato is cultivating in several areas of the country. Among them, tomato farming in Badulla significantly contributes to the total local tomato production. However, the producer price of Badulla tomato is subjected to the fluctuation within a short period of time. Hence, farmers face great difficulties when selling their products. This study was aimed to explore the influence of weather factors on Badulla tomato price fluctuation. The data was collected from the Meteorological Department of Sri Lanka and the Hector Kobbekaduwa Agrarian and Research Institute for the past 10 years (2005-2015). These are the considered Badulla district weather factors: rainfall (BR), minimum (MinTB)/maximum (MxTB) temperature, minimum (MinRH)/maximum (MxRH) relative humidity and the farm gate price of tomato at Badulla district (BTP). The Data set was consisted only quantitative data and there were 574 instances. Analysis and investigation were done using data mining techniques. After preprocessing of data, 66% percentage from the total number of instances were considered as training data. The K-Means algorithm was used to cluster the above data vectors. The Euclidean distance function was used to compare the data vectors. The strength of K-Means clustering was validated using Elbow cluster validation technique. Five clusters were formed as the best number of clusters. Within cluster sum of squared errors: 24.68 and 15 number of iterations were performed within the clustering model. Highest Badulla tomato price centroid value was: Rs.49, other cluster centroid values were BR: 27.6mm, MxTB: 27.6 o C, MinTB: 14.2 o C, MxRH: 96.06%, MinRH: 63.1% of that cluster. As results of this research, it is possible to predict best weather conditions which are giving highest Badulla Tomato Price. That will be helpful for farmers as well as the decision makers to take correct decisions related to tomato farming.