Stock Forecasting Using an Improved Version of Adaptive Group Lasso

2020 ◽  
Vol 17 (8) ◽  
pp. 3370-3373
Author(s):  
S. Nandhini ◽  
Monojit Debnath ◽  
Siddarth Tyagi ◽  
Shashank Mishra ◽  
K. Ashok Kumar
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Linear Regression Model ◽  
Stock Price ◽  
Group Lasso ◽  
Stock Prediction ◽  
The World ◽  
The Future ◽  
Penalization Technique

Stock is one of the few things in the world that influence the economy of the society, hence its worth a shot and very desirable to predict a stock price in the future. Hence in this paper we propose a stock prediction model based on linear regression model. The database of the training is based on the Goldman Sachs database of stocks found from the Google. Here we choose Lasso penalization technique cause the, this performs well with the sparsity of the network, meaning when the network has less features and more observations. Here we have proposed an improved version of lasso function and have proposed an algorithm to improve the performance of the model.

scholarly journals Learning Discriminative Recommendation Systems with Side Information

2017 ◽  
Author(s):  
Feipeng Zhao ◽  
Yuhong Guo
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Real World ◽  
Linear Regression Model ◽  
Side Information ◽  
Recommendation Systems ◽  
Joint Model ◽  
Partially Observed ◽  
Real World Applications

Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems. This joint model aggregates observed user-item recommendation activities to produce the missing user-item recommendation scores while simultaneously training a linear regression model to predict the user-item recommendation scores from auxiliary item features. We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems.

scholarly journals Forecasting cumulative COVID-19 cases in Malaysia and rising to unprecedented levels

2021 ◽  
Vol 20 (3) ◽  
pp. 504-510
Author(s):  
Wan Muhamad Amir W Ahmad ◽  
Mohamad Arif Awang Nawi ◽  
Wan Mohd Nazlee Wan Zainon ◽  
Nor Farid Mohd Noor ◽  
Firdaus Mohd Hamzah ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Sharp Increase ◽  
Linear Trend ◽  
Medical Science ◽  
Predictive Modelling ◽  
Ministry Of Health ◽  
The World

Background: COVID-19 outbreak is being studied throughout the world. Adding more analysis to date strengthening the information about the illness. Here, we analysis the data of Malaysian Ministry of Health from February 15, 2020 until January 10, 2021 was analysed using linear regression model statistical analysis with aim to forecast the trend. Materials and Methods: This study reviewed the data by Malaysia Ministry of Health from February 15, 2020, until January 10, 2021. Linear regression model statistical analysis was used for predictive modelling. The forecasting of the linear trend of the Covid-19 outbreak prediction is purposed to estimate the number of confirm cases according to the number of recoveries patients. Results: Malaysia is currently anticipating another lockdown restriction as new confirmed case of COVID-19 hit new record high. The cumulative confirmed Covid-19 cases in MCO predicted a sharp increase. At the first of March, 2021, the predicted cumulative confirmed Covid-19 cases are 319,477 cases. Conclusions: Covid-19 cases projected to 315766 by end of February 2021 with 3000- 4000 daily cases predicted. Initiative and proactive measurement by Malaysian government hopefully can reduce the number of cases and flatten the infection curve. Bangladesh Journal of Medical Science Vol.20(3) 2021 p.504-510

scholarly journals Estimating global case fatality rate of coronavirus disease 2019 (COVID-19) pandemic

2020 ◽  
Author(s):  
Manisha Mandal ◽  
Shyamapada Mandal
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Model Analysis ◽  
World Health ◽  
Fatality Rate ◽  
Statistical Regression ◽  
The World

Abstract There is a huge loss of lives worldwide in relation to COVID-19 pandemic, the primary epicentre of which is China, where the causative agent of the disease called SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) was first emerged in December 2019. In view of this the severity, in terms of case fatality rate (CFR), is essential to explore for COVID-19. Data of ongoing COVID-19 global pandemic have been retrieved from publicly accessible website of the WHO (World Health Organization), and were processed for the estimation of global (both including and excluding China) CFRs of COVID-19. The CFRs were explored following the naive estimates, 14-day delay estimates, and linear regression model analysis, for the period between January 25, 2020 and April 25, 2020, on weekly basis. To explore the current situation, in terms of global CFR, data for the next 6 weeks (May 2, 2020 through June 6, 2020), were processed by naive and linear regression model analysis. Mean CFRs, in the naive estimates, were 4.59% (95% CI: 3.59 – 5.59) for the world including China, and 3.62% (95% CI: 2.01 – 5.23) for the world excluding China. The 14-day delay estimates of CFRs were 15.6% (95% CI: 7.18 – 24.02) globally, and 21.65% (95% CI: 11.15 – 32.15) in countries outside China. Following statistical model analysis, the global (both including and excluding China) CFRs were 6.81%, by naive estimates, and ~13%, by 14-day delay estimates. The global CFR of COVID-19 during May 2, 2020 to June 6, 2020, ranged 5.9 – 7.04% (mean: 6.58%; 95% CI: 6.11 – 7.03), by naive estimates, and by statistical regression analysis the CFR was 4.78%. The CFR as explored in the current study might help estimate the need of up-to-date hospital supplies and other mitigation measures for COVID-19 ongoing pandemic.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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