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Prediction of corn price fluctuation based on multiple linear regression analysis model under big data
6 references·0 citing·32 second hop
Prediction of corn price fluctuation based on multiple linear regression analysis model under big data (2019)Prediction of corn price fluctuation base…Performance Analysis of Four Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity Futures Price Forecasting (2017)Performance Analysis of Four …Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks (2017)Assess and prognosticate gree…A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing (2017)A new method of large-scale s…Worldwide Acreage and Yield Response to International Price Change and Volatility: A Dynamic Panel Data Analysis for Wheat, Rice, Corn, and Soybeans (2016)Worldwide Acreage and Yield R…Worldwide Acreage and Yield Response to International Price Change and Volatility: A Dynamic Panel Data Analysis for Wheat, Rice, Corn, and Soybeans (2015)Worldwide Acreage and Yield R…Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales (2015)Detrended fluctuation analysi…Detecting long-range correlations with detrended fluctuation analysis (2001)Multifractal detrended fluctuation analysis of nonstationary time series (2002)Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Nonstationary Time Series (2008)Joint multifractal analysis based on the partition function approach: analytical analysis, numerical simulation and empirical application (2015)Cross-correlations between volatility, volatility persistence and stock market integration: the case of emergent stock markets (2016)Detrended Multiple Cross-Correlation Coefficient (2018)Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations (1991)Initial conditions and moment restrictions in dynamic panel data models (1998)How to do Xtabond2: An Introduction to Difference and System GMM in Stata (2009)An Overview of Global Wheat Market Fundamentals in an Era of Climate Concerns (2017)Network analysis of corn cash price comovements (2021)Network Analysis of Price Comovements Among Corn Futures and Cash Prices (2022)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)Determining Optimal Lag Time Selection Function with Novel Machine Learning Strategies for Better Agricultural Commodity Prices Forecasting in Malaysia (2020)Developing sustainable competitive advantages from the lens of resource-based view: evidence from IT sector of an emerging economy (2022)Long term and short term forecasting of horticultural produce based on the LSTM network model (2022)The Measurement of Productive Efficiency (1957)Measuring the efficiency of decision making units (1978)Data envelopment analysis (DEA) – Thirty years on (2008)Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends (2018)Evaluation of the green technology innovation efficiency of China's manufacturing industries: DEA window analysis with ideal window width (2018)Does industrial agglomeration facilitate environmental performance: New evidence from urban China? (2019)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)A novel hybridization of artificial neural networks and ARIMA models for time series forecasting (2010)Modeling and Forecasting Commodity Futures Prices: Decomposition Approach (2022)Research on carbon asset trading strategy based on PSO-VMD and deep reinforcement learning (2023)Various optimized machine learning techniques to predict agricultural commodity prices (2024)A digitális innovációk hatása az élelmiszer-kiskereskedelmi vállalatok teljesítményére (2019)Weather shocks, traders' expectations, and food prices (2021)
this work references cited by second hop

References

  • 2015Worldwide Acreage and Yield Response to International Price Change and Volatility: A Dynamic Panel Data Analysis for Wheat, Rice, Corn, and Soybeans141 cited
  • 2017Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks119 cited
  • 2015Detrended fluctuation analysis as a regression framework: Estimating dependence at different scales84 cited
  • 2016Worldwide Acreage and Yield Response to International Price Change and Volatility: A Dynamic Panel Data Analysis for Wheat, Rice, Corn, and Soybeans72 cited
  • 2017A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing69 cited
  • 2017Performance Analysis of Four Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity Futures Price Forecasting51 cited

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