Application of Improved LSTM Algorithm in Macroeconomic Forecasting

From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government’s economic forecast and macro control. Therefore, it is necessary to strengthen the resea...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shijun Chen, Xiaoli Han, Yunbin Shen, Chong Ye
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c54bfa07755d44018b64cba36843aca0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c54bfa07755d44018b64cba36843aca0
record_format	dspace
spelling	oai:doaj.org-article:c54bfa07755d44018b64cba36843aca02021-11-08T02:37:26ZApplication of Improved LSTM Algorithm in Macroeconomic Forecasting1687-527310.1155/2021/4471044https://doaj.org/article/c54bfa07755d44018b64cba36843aca02021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4471044https://doaj.org/toc/1687-5273From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government’s economic forecast and macro control. Therefore, it is necessary to strengthen the research on early warning and prediction of agricultural futures price. For the prediction of futures price, there are two kinds of common models: one is the traditional classic time series model, and the other is the neural network model under the wave of artificial intelligence. This paper selects the 1976 closing data of agricultural futures index from January 10, 2012, to February 27, 2020, and uses the time series differential autoregressive integrated moving average model (ARIMA model) and long short-term memory model (LSTM model) to study this work, respectively, and compares the predicted effects of the two models in some metrics. Based on the predicted results of the two models, a simple trading strategy is established, and the trading effects of the two models are compared. The results show that the LSTM model has obvious advantage over ARIMA time series model in the price index prediction of agricultural futures market.Shijun ChenXiaoli HanYunbin ShenChong YeHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Shijun Chen Xiaoli Han Yunbin Shen Chong Ye Application of Improved LSTM Algorithm in Macroeconomic Forecasting
description	From a macro perspective, futures index of agricultural products can reflect the trend of macroeconomy and can also have an early warning effect on the possible crisis and provide a reference for the government’s economic forecast and macro control. Therefore, it is necessary to strengthen the research on early warning and prediction of agricultural futures price. For the prediction of futures price, there are two kinds of common models: one is the traditional classic time series model, and the other is the neural network model under the wave of artificial intelligence. This paper selects the 1976 closing data of agricultural futures index from January 10, 2012, to February 27, 2020, and uses the time series differential autoregressive integrated moving average model (ARIMA model) and long short-term memory model (LSTM model) to study this work, respectively, and compares the predicted effects of the two models in some metrics. Based on the predicted results of the two models, a simple trading strategy is established, and the trading effects of the two models are compared. The results show that the LSTM model has obvious advantage over ARIMA time series model in the price index prediction of agricultural futures market.
format	article
author	Shijun Chen Xiaoli Han Yunbin Shen Chong Ye
author_facet	Shijun Chen Xiaoli Han Yunbin Shen Chong Ye
author_sort	Shijun Chen
title	Application of Improved LSTM Algorithm in Macroeconomic Forecasting
title_short	Application of Improved LSTM Algorithm in Macroeconomic Forecasting
title_full	Application of Improved LSTM Algorithm in Macroeconomic Forecasting
title_fullStr	Application of Improved LSTM Algorithm in Macroeconomic Forecasting
title_full_unstemmed	Application of Improved LSTM Algorithm in Macroeconomic Forecasting
title_sort	application of improved lstm algorithm in macroeconomic forecasting
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/c54bfa07755d44018b64cba36843aca0
work_keys_str_mv	AT shijunchen applicationofimprovedlstmalgorithminmacroeconomicforecasting AT xiaolihan applicationofimprovedlstmalgorithminmacroeconomicforecasting AT yunbinshen applicationofimprovedlstmalgorithminmacroeconomicforecasting AT chongye applicationofimprovedlstmalgorithminmacroeconomicforecasting
_version_	1718443038831804416

Application of Improved LSTM Algorithm in Macroeconomic Forecasting

Ejemplares similares