scholarly journals Construction of a Predictive Model for MLB Matches

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 102-112
Author(s):  
Chia-Hao Chang
Keyword(s):  
Markov Process ◽  
Prediction Model ◽  
Equilibrium Point ◽  
Predictive Model ◽  
Major League Baseball ◽  
Market Equilibrium ◽  
Accurate Prediction ◽  
Expected Return ◽  
Betting Odds ◽  
Process Method

The main purpose of this article was to define a model that could defeat the online bookmakers’ odds, where the betting item considered was the first five innings of major league baseball (MLB) matches. The betting odds of online bookmakers have two purposes: first, they are used to quantify the amount of profit made by the bettors; second, they are regarded as a market equilibrium point between multiple bookmakers and bettors. If the bettors have a more accurate prediction model than the system used to produce betting odds, it will create a positive expected return for the bettors. In this article, we used the Markov process method and the runner advancement model to estimate the expected runs in an MLB match for the teams based on the batting lineup and the pitcher.

scholarly journals ANALISIS UJI AKURASI MODEL GROVER, SPRINGATE, DAN ZMIJEWSKI DALAM MEMPREDIKSI KEBANGKRUTAN PERUSAHAAN DELISTED DI BEI

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Komang Agus Rudi Indra Laksmana ◽  
Ayu Darmawati
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Predictive Model ◽  
Financial Distress ◽  
Bankruptcy Prediction ◽  
Accurate Prediction ◽  
Accuracy Rate ◽  
The Future

This study aimed at analyzing how the results of the Grover, Springate and Zmijewski models predict the bankruptcy of PT Citra Maharlika Nusantara Corpora Tbk for the period of June 2013 - September 2016. This study also aimed at measuring the accuracy of the bankruptcy prediction model and determined which predictive model of the three models was the most accurate. From the data analysis, it was found that Springate model was the most accurate prediction model with 100% accuracy rate to predict the bankruptcy of PT Citra Maharlika Nusantara Corpora Tbk compared to the Grover model with an accuracy rate of 71.48% and Zmijewski model with the lowest accuracy rate of 21.48%. The limitations of this study was this study only carried out in one company, thus in the future it is expected that the model will be tested in more than one company and type of business sector.Keywords: Financial Distress, Grover, Springate, Zmijewski ModelsPenelitian ini bertujuan untuk menganalisis bagaimana hasil dari model Grover, Springate dan Zmijewski dalam memprediksi kebangkrutan PT Citra Maharlika Nusantara Corpora Tbk periode Juni 2013 – September 2016 serta mengukur tingkat akurasi model prediksi kebangkrutan tersebut dan menentukan model prediksi manakah diantara ketiga model tersebut yang paling akurat. Model Springate menjadi model prediksi paling akurat dengan tingkat akurasi 100% untuk memprediksi kebangkrutan PT Citra Maharlika Nusantara Corpora Tbk dibandingakan dengan model Grover dengan tingkat akurasi 71,48% dan model Zmijewski dengan tingkat akurasi paling rendah sebesar 21,48%.Keterbatasan penelitian ini terletak pada pengujian model pada satu perusahaan di satu unit sektor usaha, kedepan bisa dilakukan pengujian pada berbagai jenis sektor usaha.Kata kunci: Financial Distress, Model Grover, Springate, Zmijewski

Thermal Error Prediction of the Spindle Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 789-790 ◽  
pp. 263-267
Author(s):  
Yan Lei Li ◽  
Ming Yan Wang ◽  
You Min Hu ◽  
Bo Wu
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Temperature Data ◽  
Current Sensor ◽  
Error Prediction ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

Research of High-Speed Railway Running Economic Benefit Based on Gray Model

2012 ◽  
Vol 253-255 ◽  
pp. 1273-1277
Author(s):  
Xue Dong Du ◽  
Na Ren
Keyword(s):  
Data Analysis ◽  
Prediction Model ◽  
Economic Benefit ◽  
High Speed ◽  
Accurate Prediction ◽  
Gray Model ◽  
Practical Application ◽  
High Speed Railway ◽  
Railway Stations ◽  
Train Operation

The research of high-speed railway running economic benefit is important to timely know well the train operation state for the railway administration. A prediction model of high-speed railway running economic benefit is proposed in this article based on Gray model. The Gray model is a good example to make accurate prediction of the development of matters. According to the data analysis of Beijing and Shanghai railway stations, we can know that the result of prediction model is accurate, so the prediction based on Gray model is scientific and reasonable in the practical application.

A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning

2021 ◽  
pp. 20210525
Author(s):  
Daisuke Kawahara ◽  
Yuji Murakami ◽  
Shigeyuki Tani ◽  
Yasushi Nagata
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Esophageal Cancer ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Predictive Model ◽  
Squamous Cell ◽  
Locally Advanced ◽  
Degree Of Differentiation ◽  
Planning Ct

Objective: To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning. Methods: Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets: 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors. The radiomics features were used for the input data in the machine learning. To build predictive models with radiomics features, neural network classifiers was used. The precision, accuracy, sensitivity by generating confusion matrices, the area under the curve (AUC) of receiver operating characteristic curve were evaluated. Results: By the LASSO analysis of the training data, we found 13 radiomics features from CT images for the classification. The accuracy of the prediction model was highest for using only CT radiomics features. The accuracy, specificity, and sensitivity of the predictive model were 85.4%, 88.6%, 80.0%, and the AUC was 0.92. Conclusion: The proposed predictive model showed high accuracy for the classification of the degree of the differentiation of esophagus cancer. Because of the good prediction ability of the method, the method may contribute to reducing the pathological examination by biopsy and predicting the local control. Advances in knowledge: For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics analysis.

A predictive model of seasonal changes in herbage digestibility

1985 ◽  
Vol 105 (3) ◽  
pp. 505-512 ◽  
Author(s):  
A. W. Illius
Keyword(s):  
Predictive Model ◽  
Perennial Ryegrass ◽  
Seasonal Changes ◽  
Quantitative Description ◽  
Accurate Prediction ◽  
Nitrogen Level ◽  
Physiological Maturity

SUMMARYA quantitative description of the factors underlying seasonal changes in herbage digestibility is developed and applied to sets of data from S. 24 and S. 23 perennial ryegrass. A new variable, relative maturity, is used to describe the effect of defoliation in interrupting the process of tiller maturation which leads to the decline in digestibility. For S. 24, the model explained 95·5% of variation in digestibility decline, R.S.D. = 1·07, allowing accurate prediction of digestibility. For S. 23, 87·5% of variation was explained, R.S.D. = 1·85. The model for S. 24 also worked well on data from a different site, nitrogen level and cutting regime, and the question of the model's generality is discussed. Relative maturity appears to be a useful concept in describing the physiological maturity of swards under different harvesting regimes.

scholarly journals Variation-Oriented Data Filtering for Improvement in Model Complexity of Air Pollutant Prediction Model

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Chi Man Vong ◽  
Weng Fai Ip ◽  
Pak Kin Wong
Keyword(s):  
Prediction Model ◽  
Air Pollutants ◽  
Prediction Models ◽  
Air Pollutant ◽  
Accurate Prediction ◽  
Model Complexity ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Training Time ◽  
Model Size

Accurate prediction models for air pollutants are crucial for forecast and health alarm to local inhabitants. In recent literature,discrete wavelet transform(DWT) was employed to decompose a series of air pollutant levels, followed by modeling usingsupport vector machine(SVM). This combination of DWT and SVM was reported to produce a more accurate prediction model for air pollutants by investigating different levels of frequency bands. However, DWT has a significant demand in model complexity, namely, the training time and the model size of the prediction model. In this paper, a new method calledvariation-oriented filtering(VF) is proposed to remove the data with low variation, which can be considered asnoiseto a prediction model. By VF, the noise and the size of the series of air pollutant levels can be reduced simultaneously and hence so are the training time and model size. The SO2(sulfur dioxide) level in Macau was selected as a test case. Experimental results show that VF can effectively and efficiently reduce the model complexity with improvement in predictive accuracy.

A prediction model for pathological findings after neoadjuvant chemoradiotherapy for resectable locally advanced esophageal cancer based on PET images using radiomics and machine-learning.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 456-456
Author(s):  
Yuji Murakami ◽  
Yasushi Nagata ◽  
Daisuke Kawahara
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Predictive Model ◽  
Test Data ◽  
Locally Advanced ◽  
Neoadjuvant Chemoradiotherapy ◽  
Image Data ◽  
Ct Images ◽  
Pathological Findings ◽  
Planning Ct

456 Background: The pathologic complete response (PCR) rate by neoadjuvant chemoradiotherapy (NCRT) for resectable locally advanced esophageal squamous cell carcinoma (ESCC) is about 40%. If we could predict a PCR from pre-treatment image data, it might be possible to select patients who can be cured by organ-preserving CRT. The purpose of this study is to construct a predictive model for PCR by NCRT in patients with locally advanced ESCC using radiomics and machine-learning. Methods: We used data of 98 ESCC patients who underwent NCRT and surgery from 2003 to 2016. Firstly, we fused the radiotherapy treatment planning CT images and PET images scanned before treatment. Then using target delineations on planning CT images, we created eight kinds of target regions on PET images. Secondly, we generated a total of 6968 features per patient using the PET image data within these target regions that were preprocessed by radiomics technique. Among them, we extracted the optimal features for machine-learning using the least absolute shrinkage and selection operator (LASSO) logistic regression. Thirdly, artificial neural networks were used as a machine-learning method to create a predictive model. The extracted radiomics features were used as input values, and the information of ‘PCR’ or ‘not PCR’ was used as output values. We used data of randomly selected 58 patients for training and constructed a predictive model. Then we used data of 15 patients to validate the models and created the optimal model. Finally, we evaluated the predictive model using the test data of 25 patients. Results: By the LASSO analysis, 32 radiomics features were extracted for machine-learning classification. This predictive model predicted pathological findings after NCRT in 24 of 25 test data. The accuracy, specificity and sensitivity in the prediction of PCR after NCRT by this predictive model were 96.0%, 93.8%, and 100%, respectively. Conclusions: A prediction model based on PET images using radiomics and machine-learning could predict pathological findings after NCRT for resectable locally advanced ESCC.

MARKET EQUILIBRIUM WITH CAPITAL LOSS DEDUCTION OPTIONS

2001 ◽  
Vol 04 (05) ◽  
pp. 783-803 ◽  
Author(s):  
AUSTIN MURPHY
Keyword(s):  
Theoretical Model ◽  
Asset Pricing ◽  
Transaction Costs ◽  
Market Equilibrium ◽  
Expected Return ◽  
Asset Pricing Anomalies ◽  
Pricing Anomalies ◽  
Tax Loss ◽  
Tax Deduction

This research builds on a widely-cited study to prove that the permissible tax loss deduction subsidizes investments in volatile securities by materially lowering the required expected return on more volatile assets. The implications of the theory are robust to the existence of transaction costs, dividends, forced liquidations, and a ceiling on capital loss deductions in some countries. It is further shown that special tax treatment at death significantly increases the value of the tax deduction option. The theoretical model is explained to be consistent with empirical findings reported in the literature and to actually help explain some asset pricing anomalies.

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