Price Prediction of Agricultural Products Based on Wavelet Analysis-LSTM

China is a big agricultural country; the fluctuation of agricultural products price impact people's financial life. Due to the natural climate, accidental event and other factors, the agricultural products price is unstable and changes quickly, it is difficult to predict agricultural products price. This paper takes Fuzhou cabbage as an example, and Wavelet Analysis (WA) is used to reduce noise of the cabbage data. And then normalize the data with the fine-tuned normalization. Finally, the normalized data are fed into Long Short-Term Memory (LSTM) model for prediction. The new model named WA-LSTM based on both WA and LSTM can obtain better results than the classical LSTM model. The experiments show that this model achieved better performance and accuracy.

[1]  Jarmo Partanen,et al.  Forecasting electricity price and demand using a hybrid approach based on wavelet transform, ARIMA and neural networks , 2014 .

[2]  Girish K. Jha,et al.  Agricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System , 2013 .

[3]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[4]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[5]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[6]  Sin-Horng Chen,et al.  An RNN-based prosodic information synthesizer for Mandarin text-to-speech , 1998, IEEE Trans. Speech Audio Process..

[7]  A. Geva,et al.  Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering , 1998, IEEE Transactions on Biomedical Engineering.

[8]  Haibo Zhou,et al.  Research on agricultural product price forecasting model based on improved BP neural network , 2018, Journal of Ambient Intelligence and Humanized Computing.

[9]  Borhan Molazem Sanandaji,et al.  Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting , 2017, ArXiv.

[10]  Yong Jin,et al.  Transient density signal analysis and two-phase micro-structure flow in gas-solids fluidization , 2001 .

[11]  Quanyin Zhu,et al.  Price forecasting for agricultural products based on BP and RBF Neural Network , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering.

[12]  B. V. Chinnappa Reddy,et al.  Application of ARIMA Model for Forecasting Agricultural Prices , 2017 .

[13]  Liu Jiajia,et al.  Agricultural price fluctuation model based on SVR , 2017, 2017 9th International Conference on Modelling, Identification and Control (ICMIC).

[14]  Li Pan,et al.  Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

[15]  Alexey Ozerov,et al.  Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  Hu Zhen,et al.  Application of the Grey System Theory in the Forest Land Forecasting , 2006 .

[17]  R Malladi,et al.  Image processing via level set curvature flow. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[19]  Qi-Rong Qiu,et al.  Short-term wind speed forecasting combined time series method and arch model , 2012, 2012 International Conference on Machine Learning and Cybernetics.

[20]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[21]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[22]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[23]  Mahdi Jalili,et al.  Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting , 2019, EANN.

[24]  S GuptaG.  Arima Model for and Forecasts on Tea Production in India , 1993 .

[25]  LI Chuan-xi,et al.  Application of ARIMA model in forecasting agricultural product price , 2009 .