Table 13 Forecast accuracy for corn,... | Emerald Publishing

Table 13

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

		Corn		Soybeans		Wheat
Horizon	Method	RMSE	MAPE(%)	RMSE	MAPE(%)	RMSE	MAPE(%)
h = 0
	LSTM	0.783	14.415	1.339	9.689	0.944	12.883
	LSTM	(0.004)	(0.088)	(0.007)	(0.063)	(0.005)	(0.096)
	CNN-LSTM	1.497	28.687	2.579	19.695	1.515	22.957
	CNN-LSTM	(0.023)	(0.361)	(0.056)	(0.356)	(0.034)	(0.43)
	ED-LSTM	1.37	21.555	2.488	17.772	1.45	20.43
	ED-LSTM	(0.003)	(0.093)	(0.008)	(0.081)	(0.004)	(0.08)
h = 1
	LSTM	0.413	6.336	0.806	5.917	0.408	6.386
	LSTM	(0.002)	(0.051)	(0.006)	(0.05)	(0.003)	(0.06)
	CNN-LSTM	1.383	24.689	2.459	17.355	1.35	19.81
	CNN-LSTM	(0.025)	(0.353)	(0.061)	(0.318)	(0.031)	(0.376)
	ED-LSTM	1.155	19.077	1.956	14.863	1.077	15.615
	ED-LSTM	(0.004)	(0.09)	(0.009)	(0.08)	(0.005)	(0.09)
h = 2
	LSTM	0.364	6.166	0.627	5.04	0.338	5.169
	LSTM	(0.003)	(0.062)	(0.005)	(0.044)	(0.003)	(0.053)
	CNN-LSTM	1.26	20.179	2.334	15.648	1.237	16.685
	CNN-LSTM	(0.028)	(0.376)	(0.06)	(0.325)	(0.034)	(0.387)
	ED-LSTM	1.216	23.728	1.971	16.764	1.136	18.52
	ED-LSTM	(0.005)	(0.115)	(0.01)	(0.096)	(0.005)	(0.094)
h = 3
	LSTM	0.387	6.845	0.689	5.677	0.41	6.09
	LSTM	(0.003)	(0.074)	(0.007)	(0.061)	(0.004)	(0.061)
	CNN-LSTM	1.329	21.854	2.301	15.553	1.243	16.584
	CNN-LSTM	(0.03)	(0.373)	(0.061)	(0.322)	(0.033)	(0.326)
	ED-LSTM	1.329	26.649	2.129	18.316	1.279	21.685
	ED-LSTM	(0.005)	(0.126)	(0.011)	(0.115)	(0.005)	(0.103)
h = 4
	LSTM	1.182	23.034	1.97	15.253	1.241	19.306
	LSTM	(0.006)	(0.152)	(0.014)	(0.102)	(0.008)	(0.14)
	CNN-LSTM	1.572	28.339	2.777	19.75	1.548	21.183
	CNN-LSTM	(0.022)	(0.379)	(0.048)	(0.29)	(0.025)	(0.35)
	ED-LSTM	1.424	29.435	2.494	21.182	1.492	25.403
	ED-LSTM	(0.006)	(0.136)	(0.011)	(0.112)	(0.005)	(0.109)

		Corn		Soybeans		Wheat
Horizon	Method	RMSE	MAPE(%)	RMSE	MAPE(%)	RMSE	MAPE(%)
h = 0
	LSTM	0.783	14.415	1.339	9.689	0.944	12.883
	LSTM	(0.004)	(0.088)	(0.007)	(0.063)	(0.005)	(0.096)
	CNN-LSTM	1.497	28.687	2.579	19.695	1.515	22.957
	CNN-LSTM	(0.023)	(0.361)	(0.056)	(0.356)	(0.034)	(0.43)
	ED-LSTM	1.37	21.555	2.488	17.772	1.45	20.43
	ED-LSTM	(0.003)	(0.093)	(0.008)	(0.081)	(0.004)	(0.08)
h = 1
	LSTM	0.413	6.336	0.806	5.917	0.408	6.386
	LSTM	(0.002)	(0.051)	(0.006)	(0.05)	(0.003)	(0.06)
	CNN-LSTM	1.383	24.689	2.459	17.355	1.35	19.81
	CNN-LSTM	(0.025)	(0.353)	(0.061)	(0.318)	(0.031)	(0.376)
	ED-LSTM	1.155	19.077	1.956	14.863	1.077	15.615
	ED-LSTM	(0.004)	(0.09)	(0.009)	(0.08)	(0.005)	(0.09)
h = 2
	LSTM	0.364	6.166	0.627	5.04	0.338	5.169
	LSTM	(0.003)	(0.062)	(0.005)	(0.044)	(0.003)	(0.053)
	CNN-LSTM	1.26	20.179	2.334	15.648	1.237	16.685
	CNN-LSTM	(0.028)	(0.376)	(0.06)	(0.325)	(0.034)	(0.387)
	ED-LSTM	1.216	23.728	1.971	16.764	1.136	18.52
	ED-LSTM	(0.005)	(0.115)	(0.01)	(0.096)	(0.005)	(0.094)
h = 3
	LSTM	0.387	6.845	0.689	5.677	0.41	6.09
	LSTM	(0.003)	(0.074)	(0.007)	(0.061)	(0.004)	(0.061)
	CNN-LSTM	1.329	21.854	2.301	15.553	1.243	16.584
	CNN-LSTM	(0.03)	(0.373)	(0.061)	(0.322)	(0.033)	(0.326)
	ED-LSTM	1.329	26.649	2.129	18.316	1.279	21.685
	ED-LSTM	(0.005)	(0.126)	(0.011)	(0.115)	(0.005)	(0.103)
h = 4
	LSTM	1.182	23.034	1.97	15.253	1.241	19.306
	LSTM	(0.006)	(0.152)	(0.014)	(0.102)	(0.008)	(0.14)
	CNN-LSTM	1.572	28.339	2.777	19.75	1.548	21.183
	CNN-LSTM	(0.022)	(0.379)	(0.048)	(0.29)	(0.025)	(0.35)
	ED-LSTM	1.424	29.435	2.494	21.182	1.492	25.403
	ED-LSTM	(0.006)	(0.136)	(0.011)	(0.112)	(0.005)	(0.109)

Source(s): Created by the authors