Tag Archives: Prediction

Stochastic Modelling/Game Theory Analysis of Scoreline (Published)

Prediction of a football match result arouses interest from different points of view; for different people, Hence the need for this work which aims at analysing the scores of the four top English clubs to enable prediction of future outcome of matches to be made in a more scientific manner. From the analysis of the scoreline of the top four EPL clubs; Manchester United (M.U), Chelsea (C), Arsenal (A), and Manchester City (M.C) from (2002-2015) using Game theory and Stochastic modelling, Chelsea emerged the best team with a selection probability of  0.41  while Manchester United also emerged second best with a selection probability of 0.37. From the steady state transition probability matrix, for all the six possible pairs of the four clubs shows that the probability of M.U wining C at home is 0.44 while C wins M.U at home with probability  0.67 depicting C as the stronger club. Similarly M.U is stronger than A, with a 0.71 winning probability as against 0.25 winning probability for A, while M.U and M.C appears to be equally matched with 0.48 and 0.49 probability of winning. C against A reveals a probability of 0.58 and 0.25 for A vs C. while C vs M.C showed C to have an upper hand with a 0.71 probability of winning and 0.44 for M.C vs C. Finally A vs M.C gives the two teams 0.53 and 0.42 winning probabilities. Thus, the two most viable clubs out of the four clubs are Manchester United and Chelsea. Using the four step TPM we also predicted the 2015/2016 matches to obtain their various probabilities given the previous game.

Models and Quality Control Charts for the Prediction of Compressive Cement Strength (Published)

This paper presents quality control charts techniques usually applied in quality control of compressive cement strength. Nonlinear regression model useful for the prediction of compressive cement strength at 28 days was proposed. Combining the prediction and quality control tools, a PI (Proportional Integral) controller useful for the regulation of 28 days compressive cement strength around a target (39 Mpa) was constructed. Results of the one-year prediction of quarterly compressive cement strength aligned with the values of the historical data obtained from a leading Cement Company in Nigeria for the years, 2011-2015.

Stochastic Modeling/Game Theory Analysis of Scoreline (Published)

Prediction of a football match result arouses interest from different points of view; for different people, Hence the need for this work which aims at analysing the scores of the four top English clubs to enable prediction of future outcome of matches to be made in a more scientific manner. From the analysis of the scoreline of the top four EPL clubs; Manchester United (M.U), Chelsea (C), Arsenal (A), and Manchester City (M.C) from (2002-2015) using Game theory and Stochastic modelling, Chelsea emerged the best team with a selection probability of  0.41  while Manchester United also emerged second best with a selection probability of 0.37. From the steady state transition probability matrix, for all the six possible pairs of the four clubs shows that the probability of M.U wining C at home is 0.44 while C wins M.U at home with probability  0.67 depicting C as the stronger club. Similarly M.U is stronger than A, with a 0.71 winning probability as against 0.25 winning probability for A, while M.U and M.C appears to be equally matched with 0.48 and 0.49 probability of winning. C against A reveals a probability of 0.58 and 0.25 for A vs C. while C vs M.C showed C to have an upper hand with a 0.71 probability of winning and 0.44 for M.C vs C. Finally A vs M.C gives the two teams 0.53 and 0.42 winning probabilities. Thus, the two most viable clubs out of the four clubs are Manchester United and Chelsea. Using the four step TPM we also predicted the 2015/2016 matches to obtain their various probabilities given the previous game.

Dynamic Decision Tree Based Ensembled Learning Model to Forecast Flight Status (Published)

This paper explains the development of an enhanced predictive classifier for flight status that will reduce over fitting observed in existing models. A dynamic approach from ensemble learning technique called bagging algorithm was used to train a number of base learners using a base learning algorithm. The results of the various classifiers were combined, voting was done, by majority the most voted class was picked as the final output. This output was subjected to the decision tree algorithm to produce various replica sets generated from the training set to create various decision tree models. Object-Oriented Analysis and Design (OO-AD) methodology was adopted for the design and implementation was done with C# programming language. The result achieved was favorable as it was found to predict at an accuracy of 78.3% as against 68.2% accuracy of the existing systems which indicated an enhancement.

Earthquake Prediction in Awi, Akamkpa Local Government Area of Cross River State, Nigeria (Published)

In earthquake prediction, the changes in the physical properties of the rocks are closely monitored. The parameters that are usually monitored include seismic P velocity, ground uplift, radon emission, electrical resistivity, number of seismic events, etc. This study monitored the seismic P velocity for a period of five years (2006 to 2010) in the study area. The data shows that the travel time of the seismic P waves did not change during the period under investigation and therefore the velocity of the P waves did not change. Consequently, we do not expect an earthquake to occur in the study area in the near future

Keywords: Earthquake, P Waves, Prediction, Seismic, Velocity

Earthquake Prediction in Awi, Akamkpa Local Government Area of Cross River State, Nigeria. (Published)

In earthquake prediction, the changes in the physical properties of the rocks are closely monitored. The parameters that are usually monitored include seismic P velocity, ground uplift, radon emission, electrical resistivity, number of seismic events, etc. This study monitored the seismic P velocity for a period of five years (2006 to 2010) in the study area. The data shows that the travel time of the seismic P waves did not change during the period under investigation and therefore the velocity of the P waves did not change. Consequently, we do not expect an earthquake to occur in the study area in the near future.

Keywords: Earthquake, P Waves, Prediction, Seismic, Velocity