Prediction models of sports lottery revenue in China

2020 ◽  
pp. 002072092094058
Author(s):  
Haihong Xu ◽  
Yanmin Lin ◽  
Caiyou Zhou
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Time Series Data ◽  
Government Policies ◽  
Series Data ◽  
The Core ◽  
Cubic Curve ◽  
Sports Lottery ◽  
Curve Model ◽  
Best Fitting

Objective China's sports lottery income is the core power and important economic source of sports development, but it is affected by many complex factors such as society and economy. This paper studies the prediction model of sports lottery income, and provides data support and theoretical basis for relevant government policies Methods: Using the time series data of sports lottery revenue over the years, this paper studies the prediction model, compares the evaluation and selects the appropriate model, and uses the model to predict the future development of sports lottery revenue in China. Results: Through comparison, it is found that the cubic curve model has the best fitting effect on sports lottery income (R2 = 0.994, MAPE = 5.10). Using this model to predict sports lottery, it will exceed 500 billion yuan in 2025. Conclusion: the prediction model of sports lottery revenue in China is suitable to adopt the cubic curve model, which indicates that the sports lottery revenue in China will keep increasing.

scholarly journals ANALISIS KORELASI CURAH HUJAN DAN SUHU PERMUKAAN LAUT WILAYAH INDONESIA, SERTA IMPLIKASINYA UNTUK PRAKIRAAN CURAH HUJAN (STUDI KASUS KABUPATEN CILACAP) (CORRELATION ANALYSIS OF RAINFALL AND INDONESIA SEA SURFACE TEMPERATURE, AND ITS ...

Agromet ◽  
2007 ◽  
Vol 21 (2) ◽  
pp. 46 ◽  
Author(s):  
W. Estiningtyas ◽  
F. Ramadhani ◽  
E. Aldrian
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Negative Correlation ◽  
Prediction Models ◽  
Time Series Data ◽  
Sea Surface ◽  
Rainfall Pattern ◽  
Series Data ◽  
Rainfall Prediction

<p>Significant decrease in rainfall caused extreme climate has significant impact on agriculture sector, especialy food crops production. It is one of reason and push developing of rainfall prediction models as anticipate from extreme climate events. Rainfall prediction models develop base on time series data, and then it has been included anomaly aspect, like rainfall prediction model with Kalman filtering method. One of global parameter that has been used as climate anomaly indicator is sea surface temperature. Some of research indicate, there are relationship between sea surface temperature and rainfall. Relationship between Indonesian rainfall and global sea surface temperature has been known, but its relationship with Indonesian’s sea surface temperature not know yet, especialy for rainfall in smaller area like district. So, therefore the research about relationship between rainfall in distric area and Indonesian’s sea surface temperature and it application for rainfall prediction is needed. Based on Indonesian’s sea surface temperature time series data Januari 1982 until Mei 2006 show there are zona of Indonesian’s sea surface temperature (with temperature more than 27,6 0C) dominan in Januari-Mei and moved with specific pattern. Highest value of spasial correlation beetwen Cilacap’s rainfall and Indonesian’s sea surface temperature is 0,30 until 0,50 with different zona of Indonesian’s sea surface temperature. Highest positive correlation happened in March and July. Negative correlation is -0,30 until -0,70 with highest negative correlation in May and June. Model validation resulted correlation coeffcient 85,73%, fits model 20,74%, r2 73,49%, RMSE 20,5% and standart deviation 37,96. Rainfall prediction Januari-Desember 2007 period indicated rainfall pattern is near same with average rainfall pattern, rainfall less than 100/month. The result of this research indicate Indonesian’s sea surface temperature can be used as indicator rainfall condition in distric area, that means rainfall in district area can be predicted based on Indonesian’s sea surface temperature in zona with highest correlation in every month.</p><p>------------------------------------------------------------------</p><p>Penurunan curah hujan yang cukup signifikan akibat iklim ekstrim telah membawa dampak yang cukup signifikan pula pada sektor pertanian, terutama produksi tanaman pangan. Hal ini menjadi salah satu alasan yang mendorong semakin berkembangnya model-model prakiraan hujan sebagai upaya antipasi terhadap kejadian iklim ekstrim. Model prakiraan hujan yang pada awalnya hanya berbasis pada data time series, kini telah berkembang dengan memperhitungkan aspek anomali iklim, seperti model prakiraan hujan dengan metode filter Kalman. Salah satu indikator global yang dapat digunakan sebagai indikator anomali iklim adalah suhu permukaan laut. Dari berbagai hasil penelitian diketahui bahwa suhu permukaan laut ini memiliki keterkaitan dengan kejadian curah hujan. Hubungan curah hujan Indonesia dengan suhu permukaan laut global sudah banyak diketahui, tetapi keterkaitannya dengan suhu permukaan laut wilayah Indonesia belum banyak mendapat perhatian, terutama untuk curah hujan pada cakupan yang lebih sempit seperti kabupaten. Oleh karena itu perlu dilakukan penelitian yang mengkaji hubungan kedua parameter tersebut serta mengaplikasikannya untuk prakiraan curah hujan pada wilayah Kabupaten. Hasil penelitian berdasarkan data suhu permukaan laut wilayah Indonesia rata-rata Januari 1982 hingga Mei 2006 menunjukkan zona dengan suhu lebih dari 27,6 0C yang dominan pada bulan Januari-Mei dan bergerak dengan pola yang cukup jelas. Korelasi spasial antara curah hujan kabupaten Cilacap dengan SPL wilayah Indonesia rata-rata bulan Januari-Desember menunjukkan korelasi positip tertinggi antara 0,30 hingga 0,50 dengan zona SPL yang beragam. Korelasi tertinggi terjadi pada bulan Maret dan Juli. Sedangkan korelasi negatip berkisar antara -0,30 hingga -0,70 dengan korelasi negatip tertinggi pada bulan Mei dan Juni. Validasi model prakiraan hujan menghasilkan nilai koefisien korelasi 85,73%, fits model 20,74%, r2 sebesar 73,49%, RMSE 20,5% dan standar deviasi 37,96. Hasil prakiraan hujan bulanan periode Januari-Desember 2007 mengindikasikan pola curah hujan yang tidak jauh berbeda dengan rata-rata selama 19 tahun (1988-2006) dengan jeluk hujan kurang dari 100 mm/bulan. Hasil penelitian mengindikasikan bahwa SPL wilayah Indonesia dapat digunakan sebagai indikator untuk menunjukkan kondisi curah hujan di suatu wilayah (kabupaten), artinya curah hujan dapat diprediksi berdasarkan perubahan SPL pada zona-zona dengan korelasi yang tertinggi pada setiap bulannya.</p>

scholarly journals A Study on Estimation and Prediction of Vector Time Series Model Using Financial Big Data (Interest Rates)

2021 ◽  
Vol 12 (5) ◽  
pp. 309-316
Author(s):  
Jae-Hyun Kim, Chang-Ho An
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Prediction Model ◽  
Interest Rates ◽  
Time Series Data ◽  
Information Criteria ◽  
Series Data ◽  
Call Rate ◽  
Causality Test ◽  
Vector Autoregressive

Due to the global economic downturn, the Korean economy continues to slump. Hereupon the Bank of Korea implemented a monetary policy of cutting the base rate to actively respond to the economic slowdown and low prices. Economists have been trying to predict and analyze interest rate hikes and cuts. Therefore, in this study, a prediction model was estimated and evaluated using vector autoregressive model with time series data of long- and short-term interest rates. The data used for this purpose were call rate (1 day), loan interest rate, and Treasury rate (3 years) between January 2002 and December 2019, which were extracted monthly from the Bank of Korea database and used as variables, and a vector autoregressive (VAR) model was used as a research model. The stationarity test of variables was confirmed by the ADF-unit root test. Bidirectional linear dependency relationship between variables was confirmed by the Granger causality test. For the model identification, AICC, SBC, and HQC statistics, which were the minimum information criteria, were used. The significance of the parameters was confirmed through t-tests, and the fitness of the estimated prediction model was confirmed by the significance test of the cross-correlation matrix and the multivariate Portmanteau test. As a result of predicting call rate, loan interest rate, and Treasury rate using the prediction model presented in this study, it is predicted that interest rates will continue to drop.

scholarly journals Study on Fractal Multistep Forecast for the Prediction of Driving Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Longhai Yang ◽  
Hong Xu ◽  
Xiqiao Zhang ◽  
Shuai Li ◽  
Wenchao Ji
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Time Series Data ◽  
Back Propagation ◽  
Series Data ◽  
Vehicle Speed ◽  
Trajectory Data ◽  
Time Data ◽  
Characteristic Analysis ◽  
Real Time Data

The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.

scholarly journals Sur l’estimation des équations de CANDIDE-R

2009 ◽  
Vol 51 (4) ◽  
pp. 626-633
Author(s):  
Alban D’Amours
Keyword(s):  
Time Series ◽  
Cross Sections ◽  
Economic Model ◽  
Time Series Data ◽  
Series Data ◽  
Necessary Condition ◽  
Regional Data ◽  
Regional Models ◽  
The Core ◽  
Error Components Models

Abstract CANDIDE-R is a huge simultaneous macro-economic model which raises estimations difficulties. We avoid the problem of identification assuming that the great number of variables in our model makes it impossible that the necessary condition be not satisfied. We assume that our system converges to a solution solving this way the problem of identification. The core of the paper gives justifications of the procedure we adopted to estimate CANDIDE-R. Because of the presence of regional equations and the limited amount of regional data, we are bound to pool cross sections and time series data. We then justified the use of Zellner's approach instead of the error components models within the class of regional models built on national premises.

scholarly journals Time series forecasting for the adobe software company’s stock prices using ARIMA (BOX-JENKIN’) model

2021 ◽  
Vol 2115 (1) ◽  
pp. 012044
Author(s):  
R. Vaibhava Lakshmi ◽  
S. Radha
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Auto Correlation ◽  
Correlation Plot ◽  
Best Fitting ◽  
Fitting Model

Abstract The time series forecasting strategy, Auto-Regressive Integrated Moving Average (ARIMA) model, is applied on the time series data consisting of Adobe stock prices, in order to forecast the future prices for a period of one year. ARIMA model is used due to its simple and flexible implementation for short term predictions of future stock prices. In order to achieve stationarity, the time series data requires second-order differencing. The comparison and parameterization of the ARIMA model has been done using auto-correlation plot, partial auto-correlation plot and auto.arima() function provided in R (which automatically finds the best fitting model based on the AIC and BIC values). The ARIMA (0, 2, 1) (0, 0, 2) [12] is chosen as the best fitting model, with a very less MAPE (Mean Absolute Percentage Error) of 3.854958%.

scholarly journals The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 299
Author(s):  
Jianguo Zheng ◽  
Yilin Wang ◽  
Shihan Li ◽  
Hancong Chen
Keyword(s):  
Stock Market ◽  
Prediction Models ◽  
Time Series Data ◽  
Bat Algorithm ◽  
Stock Index ◽  
Stationary Time Series ◽  
Series Data ◽  
Polynomial Kernel ◽  
Chinese Stock Market ◽  
Ann Model

Accurate stock market prediction models can provide investors with convenient tools to make better data-based decisions and judgments. Moreover, retail investors and institutional investors could reduce their investment risk by selecting the optimal stock index with the help of these models. Predicting stock index price is one of the most effective tools for risk management and portfolio diversification. The continuous improvement of the accuracy of stock index price forecasts can promote the improvement and maturity of China’s capital market supervision and investment. It is also an important guarantee for China to further accelerate structural reforms and manufacturing transformation and upgrading. In response to this problem, this paper introduces the bat algorithm to optimize the three free parameters of the SVR machine learning model, constructs the BA-SVR hybrid model, and forecasts the closing prices of 18 stock indexes in Chinese stock market. The total sample comes from 15 January 2016 (the 10th trading day in 2016) to 31 December 2020. We select the last 20, 60, and 250 days of whole sample data as test sets for short-term, mid-term, and long-term forecast, respectively. The empirical results show that the BA-SVR model outperforms the polynomial kernel SVR model and sigmoid kernel SVR model without optimized initial parameters. In the robustness test part, we use the stationary time series data after the first-order difference of six selected characteristics to re-predict. Compared with the random forest model and ANN model, the prediction performance of the BA-SVR model is still significant. This paper also provides a new perspective on the methods of stock index forecasting and the application of bat algorithms in the financial field.

scholarly journals PREDICTION MODEL OF AMMONIA CONCENTRATION IN YELLOW-FEATHER BROILERS HOUSE DURING WINTER BASED ON EEMD-GRU

2020 ◽  
Vol 61 (2) ◽  
pp. 59-70
Author(s):  
Zeying Xu ◽  
Xiuguo Zou ◽  
Zhengling Yin ◽  
Shikai Zhang ◽  
Yuanyuan Song ◽  
...  
Keyword(s):  
Prediction Model ◽  
Time Series Data ◽  
Ventilation System ◽  
Back Propagation ◽  
Ammonia Concentration ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Central System ◽  
Mean Square Errors ◽  
Broiler House

In winter, the poor ventilation conditions in broiler houses may lead to high ammonia concentration, which affects the health of yellow-feather broilers or even causes the death of many broilers. This research used a machine learning model to predict the ammonia concentration in a broiler house during winter. After analysis, it was found that the ammonia generation in the broiler house was a gradual accumulation featured by non-linear data. After the broilers entered the broiler house for several days, and the ammonia concentration reached a certain value, a ventilation system was used for regulating the concentration. Firstly, the back-propagation (BP) neural network model and gated recurrent unit (GRU) model were used for predicting the ammonia concentration, respectively. Then, ensemble empirical mode decomposition (EEMD) was performed on the time series data of ammonia concentration in the broiler house. After that, the EEMD-GRU prediction model has been established for the intrinsic mode function (IMF) components and the temperature and humidity data in the broiler house. Finally, all component results were summarized to obtain the final prediction result. A comparison was conducted among the prediction results obtained by the above three models. The results show that the root mean square errors of the above three models are 6.2 ppm, 4.4 ppm, and 2.4 ppm, respectively, and the average absolute errors were 4.9 ppm, 2.8 ppm, and 1.6 ppm, respectively. It could be seen that the EEMD-GRU model had higher accuracy in predicting the ammonia concentration in the broiler house. The EEMD-GRU model can effectively predict the ammonia concentration in broiler houses, facilitating the feedback to the central system for timely adjustment.

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