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Table 1 The error analysis of the price forecasting of cowpea

From: A new method of large-scale short-term forecasting of agricultural commodity prices: illustrated by the case of agricultural markets in Beijing

The market

Dayanglu Agricultural and Sideline Products Wholesale Market, Chaoyang District, Beijing

Shunxin Shimen Agricultural Wholesale Market, Shunyi District, Beijing

MSE

MAE

MSE

MAE

AR model

2.871

1.255

1.918

1.029

Grey prediction model

3.336

1.357

1.067

0.703

Garch model

5.430

1.785

1.649

0.897

Time model

1.476

1.070

1.013

0.673

Space model

1.735

1.735

0.683

0.683

Mixed model

1.321

1.008

0.879

0.670

Warning model

0.884

0.876

0.764

0.727

The market

Chengbei Huilongguan commodity transaction market

Baliqiao Agricultural Wholesale Market, Tongzhou District, Beijing

MSE

MAE

MSE

MAE

AR model

1.849

1.058

2.545

1.312

Grey prediction model

4.673

1.705

2.460

1.210

Garch model

19.282

3.742

3.605

1.537

Time model

1.849

1.058

1.163

0.868

Space model

2.811

2.811

1.774

1.774

Mixed model

1.730

1.013

1.179

0.897

Warning model

1.815

1.791

0.482

0.482