Table 10 Forecast accuracy for corn,... | Emerald Publishing

Table 10

Forecast accuracy for corn, soybeans, and wheat farm price using LSTM (using expanding training window)

		Corn		Soybeans		Wheat
Horizon	Method	RMSE	MAPE(%)	RMSE	MAPE(%)	RMSE	MAPE(%)
h = 0
	LSTM	0.986	16.766	1.972	14.244	1.277	18.28
	LSTM	(0.003)	(0.068)	(0.006)	(0.047)	(0.003)	(0.063)
	CNN-LSTM	1.472	28.238	2.293	18.344	1.457	21.326
	CNN-LSTM	(0.02)	(0.339)	(0.044)	(0.271)	(0.03)	(0.334)
	ED-LSTM	1.453	21.602	2.708	18.514	1.513	20.741
	ED-LSTM	(0.005)	(0.106)	(0.01)	(0.084)	(0.005)	(0.095)
h = 1
	LSTM	0.609	10.407	1.235	9.835	0.777	11.801
	LSTM	(0.003)	(0.062)	(0.006)	(0.06)	(0.004)	(0.064)
	CNN-LSTM	1.483	27.543	2.42	18.985	1.361	19.977
	CNN-LSTM	(0.019)	(0.382)	(0.044)	(0.302)	(0.032)	(0.372)
	ED-LSTM	1.365	21.094	2.45	16.944	1.218	17.251
	ED-LSTM	(0.006)	(0.132)	(0.012)	(0.102)	(0.007)	(0.123)
h = 2
	LSTM	0.575	10.027	0.952	7.822	0.602	9.131
	LSTM	(0.003)	(0.078)	(0.006)	(0.062)	(0.003)	(0.06)
	CNN-LSTM	1.251	23.466	2.061	16.422	1.222	18.121
	CNN-LSTM	(0.02)	(0.371)	(0.042)	(0.332)	(0.033)	(0.373)
	ED-LSTM	1.494	24.961	2.564	18.769	1.338	20.601
	ED-LSTM	(0.007)	(0.152)	(0.013)	(0.118)	(0.007)	(0.127)
h = 3
	LSTM	0.703	11.865	0.957	6.982	0.659	9.615
	LSTM	(0.003)	(0.074)	(0.007)	(0.055)	(0.004)	(0.059)
	CNN-LSTM	1.186	22.463	1.823	14.072	1.08	16.304
	CNN-LSTM	(0.02)	(0.333)	(0.047)	(0.335)	(0.034)	(0.401)
	ED-LSTM	1.586	26.881	2.748	20.372	1.534	23.545
	ED-LSTM	(0.007)	(0.191)	(0.015)	(0.155)	(0.008)	(0.137)
h = 4
	LSTM	1.315	25.846	2.257	15.853	1.479	22.097
	LSTM	(0.005)	(0.133)	(0.011)	(0.106)	(0.006)	(0.115)
	CNN-LSTM	1.433	28.119	2.414	18.307	1.372	20.285
	CNN-LSTM	(0.018)	(0.373)	(0.048)	(0.349)	(0.031)	(0.383)
	ED-LSTM	1.549	28.713	2.978	22.298	1.68	26.776
	ED-LSTM	(0.008)	(0.2)	(0.016)	(0.157)	(0.01)	(0.169)

		Corn		Soybeans		Wheat
Horizon	Method	RMSE	MAPE(%)	RMSE	MAPE(%)	RMSE	MAPE(%)
h = 0
	LSTM	0.986	16.766	1.972	14.244	1.277	18.28
	LSTM	(0.003)	(0.068)	(0.006)	(0.047)	(0.003)	(0.063)
	CNN-LSTM	1.472	28.238	2.293	18.344	1.457	21.326
	CNN-LSTM	(0.02)	(0.339)	(0.044)	(0.271)	(0.03)	(0.334)
	ED-LSTM	1.453	21.602	2.708	18.514	1.513	20.741
	ED-LSTM	(0.005)	(0.106)	(0.01)	(0.084)	(0.005)	(0.095)
h = 1
	LSTM	0.609	10.407	1.235	9.835	0.777	11.801
	LSTM	(0.003)	(0.062)	(0.006)	(0.06)	(0.004)	(0.064)
	CNN-LSTM	1.483	27.543	2.42	18.985	1.361	19.977
	CNN-LSTM	(0.019)	(0.382)	(0.044)	(0.302)	(0.032)	(0.372)
	ED-LSTM	1.365	21.094	2.45	16.944	1.218	17.251
	ED-LSTM	(0.006)	(0.132)	(0.012)	(0.102)	(0.007)	(0.123)
h = 2
	LSTM	0.575	10.027	0.952	7.822	0.602	9.131
	LSTM	(0.003)	(0.078)	(0.006)	(0.062)	(0.003)	(0.06)
	CNN-LSTM	1.251	23.466	2.061	16.422	1.222	18.121
	CNN-LSTM	(0.02)	(0.371)	(0.042)	(0.332)	(0.033)	(0.373)
	ED-LSTM	1.494	24.961	2.564	18.769	1.338	20.601
	ED-LSTM	(0.007)	(0.152)	(0.013)	(0.118)	(0.007)	(0.127)
h = 3
	LSTM	0.703	11.865	0.957	6.982	0.659	9.615
	LSTM	(0.003)	(0.074)	(0.007)	(0.055)	(0.004)	(0.059)
	CNN-LSTM	1.186	22.463	1.823	14.072	1.08	16.304
	CNN-LSTM	(0.02)	(0.333)	(0.047)	(0.335)	(0.034)	(0.401)
	ED-LSTM	1.586	26.881	2.748	20.372	1.534	23.545
	ED-LSTM	(0.007)	(0.191)	(0.015)	(0.155)	(0.008)	(0.137)
h = 4
	LSTM	1.315	25.846	2.257	15.853	1.479	22.097
	LSTM	(0.005)	(0.133)	(0.011)	(0.106)	(0.006)	(0.115)
	CNN-LSTM	1.433	28.119	2.414	18.307	1.372	20.285
	CNN-LSTM	(0.018)	(0.373)	(0.048)	(0.349)	(0.031)	(0.383)
	ED-LSTM	1.549	28.713	2.978	22.298	1.68	26.776
	ED-LSTM	(0.008)	(0.2)	(0.016)	(0.157)	(0.01)	(0.169)

Source(s): Created by the authors