def multi_emd_ann(lag=3, num_trial=20, hidden=128, epochs=20):
pre_data_tf_result = pd.DataFrame() # 百分比结果
real_result = pd.DataFrame() # 预测重构值
time_ = [] # 时间
mape, mae, mse, rmse = [], [], [], []
for j in range(num_trial):
pr = None
start_time = time.time()
for i in range(len(imfs)):
d = seq_tf_matrix(imfs[i], n=lag+1)
x = d[:, :-1]
y = d[:, -1]
if pr is None: # 预测的值,子序列预测结果
pr = ann(x, y, test_num=test_num, hidden=hidden, batch_size=batch_size, epochs=epochs)
else: # 预测的值,子序列结果直接相加
pr = pr + ann(x, y, test_num=test_num, hidden=hidden, batch_size=batch_size, epochs=epochs)
end_time = time.time()
t = (end_time - start_time)
restore_value = restore_data(pr, data[-test_num - 1:-1]) # 还原预测值
mape_, mae_, mse_, rmse_ = loss_function(restore_value, data[-test_num:])
pre_data_tf_result[str(j + 1) + '_times_lag' + str(lag)] = pr
real_result[str(j + 1) + '_times_lag' + str(lag)] = restore_value
time_.append(t / 60) # 分钟
mape.append(mape_)
mae.append(mae_)
mse.append(mse_)
rmse.append(rmse_)
# 预测结果
pre_data_tf_result['test_percentage'] = data_tf[-test_num:] # 把真实的需要预测的百分比值加入
real_result['test_value'] = data[-test_num:] # 把真实的需要预测的原值加入
pre_data_tf_result.to_csv('../result/' + name_data + '/data_tf_result/lag_' + str(lag) + '_multi_emd_ann_data_tf_result.csv')
real_result.to_csv('../result/' + name_data + '/real_result/lag_' + str(lag) + '_multi_emd_ann_real_result.csv')
# 预测结果评价指标
result_evaluation = {'lag': lag, 'num_sub_sequences': len(imfs), 'time': time_, 'mape': mape,
'mae': mae, 'mse': mse, 'rmse': rmse}
fw = open('../result/' + name_data + '/multi_emd_ann_result_evaluation.json', 'a')
fw.write(json.dumps(result_evaluation) + '\n')
fw.close()
# arima(15)
# lag = [3, 4, 5, 6, 7, 8, 9]
# for lag in lag:
# only_ann(lag, 2)
# only_lstm(lag, 2)
# single_emd_ann(lag, 2)
# single_emd_lstm(lag, 2)
# multi_emd_ann(lag, 2)
# multi_emd_lstm(lag, 2)
# single_emd_lstm(3, 2, epochs=20)
# single_emd_lstm(3, 5, epochs=30)
# single_emd_ann(3, 1)
def only_lstm(lag=3, num_trial=20, hidden=128, epochs=20):
x = seq_tf_matrix(data_tf, n=lag + 1) # 转换序列成矩阵，n-1个滞后项，共n列
x = x[:, :-1]
x = np.reshape(x, (x.shape[0], x.shape[1], 1)) # 转化成输入格式
y = x[:, -1]
pre_data_tf_result = pd.DataFrame() # 变换数据预测结果
real_result = pd.DataFrame() # 变换数据预测结果重构值
time_ = [] # 时间
mape, mae, mse, rmse = [], [], [], []
for i in range(num_trial):
start_time = time.time()
pr = lstm(x,
y,
test_num=test_num,
batch_size=batch_size,
epochs=epochs,
hidden=hidden)
end_time = time.time()
restore_value = restore_data(pr, data[-test_num - 1:-1]) # 还原预测值
mape_, mae_, mse_, rmse_ = loss_function(restore_value,
data[-test_num:])
pre_data_tf_result[str(i + 1) + '_times_lag' + str(lag)] = pr
real_result[str(i + 1) + '_times_lag' + str(lag)] = restore_value
# 保存第i次的评估指标
time_.append((end_time - start_time) / 60) # 分钟
mape.append(mape_)
mae.append(mae_)
mse.append(mse_)
rmse.append(rmse_)
# 预测结果
pre_data_tf_result['test_percentage'] = data_tf[
-test_num:] # 把真实的需要预测的百分比值加入
real_result['test_value'] = data[-test_num:] # 把真实的需要预测的原值加入
pre_data_tf_result.to_csv('../' + ada_result + '/' + name_data +
'/data_tf_result/lag_' + str(lag) +
'_only_lstm_data_tf_result.csv')
real_result.to_csv('../' + ada_result + '/' + name_data +
'/real_result/lag_' + str(lag) +
'_only_lstm_real_result.csv')
# 预测结果评价指标
result_evaluation = {
'lag': lag,
'time': time_,
'mape': mape,
'mae': mae,
'mse': mse,
'rmse': rmse
}
fw = open(
'../' + ada_result + '/' + name_data +
'/only_lstm_result_evaluation.json', 'a')
fw.write(json.dumps(result_evaluation) + '\n')
fw.close()
def get_data(l, lag=3):
"""
默认滞后3项，预测一步
l 的最后一项不参与分解
"""
decomposer = EMD(l[:-1]) # l的最后一项不参与分解
imfs = decomposer.decompose() # 包括m个imf和一个res项
# 得到如下的输入样本，第一个样本（1，lag，m+1），即lag个滞后项，每一项有m+1个元素
# [[imf1_1,imf2_1,...,imfm_1,res_1],[imf1_2,imf2_2,...,imfm_2,res_2],...,[imf1_lag,imf2_lag,...,imfm_lag,res_lag]]
x = seq_tf_matrix(imfs.T, lag)
# y为输出结果，未来一步的预测值
y = l[-len(x):]
return x, y
def single_emd_ann(lag=3, num_trial=20, hidden=256, epochs=20):
x = seq_tf_matrix(imfs.T, n=lag+1) # 转换序列成矩阵，n-1个滞后项，共n列
x = x[:, :-1, :]
x = np.reshape(x, (x.shape[0], x.shape[1]*x.shape[2]))
y = data_tf[-len(x):]
pre_data_tf_result = pd.DataFrame() # 百分比结果
real_result = pd.DataFrame() # 预测重构值
time_ = [] # 时间
mape, mae, mse, rmse = [], [], [], []
for i in range(num_trial):
start_time = time.time()
pr = ann(x, y, test_num=test_num, hidden=hidden, batch_size=batch_size, epochs=epochs) # 预测的值
end_time = time.time()
restore_value = restore_data(pr, data[-test_num - 1:-1]) # 还原预测值
mape_, mae_, mse_, rmse_ = loss_function(restore_value, data[-test_num:])
# 保存第i次的结果
pre_data_tf_result[str(i+1)+'_times_lag'+str(lag)] = pr
real_result[str(i+1)+'_times_lag'+str(lag)] = restore_value
# 保存第i次的评估指标
time_.append((end_time - start_time) / 60) # 分钟
mape.append(mape_)
mae.append(mae_)
mse.append(mse_)
rmse.append(rmse_)
# 预测结果
pre_data_tf_result['test_percentage'] = data_tf[-test_num:] # 把真实的需要预测的百分比值加入
real_result['test_value'] = data[-test_num:] # 把真实的需要预测的原值加入
pre_data_tf_result.to_csv('../result/'+name_data+'/data_tf_result/lag_'+str(lag)+'_single_emd_ann_data_tf_result.csv')
real_result.to_csv('../result/'+name_data+'/real_result/lag_'+str(lag)+'_single_emd_ann_real_result.csv')
# 预测结果评价指标
result_evaluation = {'lag': lag, 'num_sub_sequences': len(imfs), 'time': time_, 'mape': mape,
'mae': mae, 'mse': mse, 'rmse': rmse}
fw = open('../result/'+name_data+'/single_emd_ann_result_evaluation.json', 'a')
fw.write(json.dumps(result_evaluation) + '\n')
fw.close()
def multi_emd_aann(lag=3, num_trial=2, hidden=128, epochs=20, ignore=ignore):
pre_data_tf_result = pd.DataFrame() # 百分比结果
real_result = pd.DataFrame() # 预测重构值
time_ = [] # 时间
mape, mae, mse, rmse = [], [], [], []
for j in range(num_trial):
result = []
start_time = time.time()
# 100(test_num)个测试样本
for k in range(test_num):
decomposer = EMD(data_tf[:-test_num + k]) # 最后一项不参与分解
imfs = decomposer.decompose() # 包括m个imf和一个res项
pr = None
for i in range(len(imfs)):
d = seq_tf_matrix(np.hstack((imfs[i], [0])),
n=lag + 1) # 给imfs[i]加上一个值作为最后一项的真实值，只占个位子
x = d[:, :-1]
if ignore:
x = x[:, :-ignore] # 忽略与预测值最近的ignore项
y = d[:, -1]
if pr is None:
pr = ann(x,
y,
test_num=1,
batch_size=batch_size,
hidden=hidden,
epochs=epochs) # 预测的值,子序列预测结果
else:
pr = pr + ann(x,
y,
test_num=1,
batch_size=batch_size,
hidden=hidden,
epochs=epochs) # 预测的值,子序列结果直接相加
result.append(pr[0])
end_time = time.time()
pr = np.array(result)
restore_value = restore_data(pr, data[-test_num - 1:-1]) # 还原预测值
mape_, mae_, mse_, rmse_ = loss_function(restore_value,
data[-test_num:])
# 保存第i次的结果
pre_data_tf_result[str(j + 1) + '_times_lag' + str(lag)] = pr
real_result[str(j + 1) + '_times_lag' + str(lag)] = restore_value
# 保存第i次的评估指标
time_.append((end_time - start_time) / 60) # 分钟
mape.append(mape_)
mae.append(mae_)
mse.append(mse_)
rmse.append(rmse_)
# 预测结果
pre_data_tf_result['test_percentage'] = data_tf[
-test_num:] # 把真实的，需要预测的百分比值加入
real_result['test_value'] = data[-test_num:] # 把真实的需要预测的原值加入
pre_data_tf_result.to_csv('../' + ada_result + '/' + name_data +
'/data_tf_result/lag_' + str(lag) +
'_multi_emd_aann_data_tf_result.csv')
real_result.to_csv('../' + ada_result + '/' + name_data +
'/real_result/lag_' + str(lag) +
'_multi_emd_aann_real_result.csv')
# 预测结果评价指标
result_evaluation = {
'lag': lag,
'time': time_,
'mape': mape,
'mae': mae,
'mse': mse,
'rmse': rmse
}
fw = open(
'../' + ada_result + '/' + name_data +
'/multi_emd_aann_result_evaluation.json', 'a')
fw.write(json.dumps(result_evaluation) + '\n')
fw.close()
path = '../data/' + name_data + '.csv' # 数据的地址
df_01 = pd.read_csv(path, encoding='gbk') # 读取数据
df_data = df_01[['Date', 'Close']].set_index('Date').iloc[::-1] # 把数据按日期排列
df_data['Close'] = df_data['Close'].astype('float64')
data = np.array(df_data)
data = np.reshape(data, (len(data), )) # 转换成（sample,）np.array
diff = data_trans(data)
# print(theta(series_minmax(data)[0]))
# print(theta(series_minmax(diff)[0]))
# print(corrcoef_imfs(series_minmax(data)[0]))
# print(corrcoef_imfs(series_minmax(diff)[0]))
print(matrix_cor(seq_tf_matrix(data, 10)))
# 上证指数
# [0.9925052213533833, 0.993386133667623, 0.9942571787506765, 0.9950962804128652, 0.9960115824395269,
# 0.9969224666313158, 0.9977260830415531, 0.9984645658860706, 0.9992588904110402, 1.0]
# 标普500
# [0.9980923986063942, 0.9982712262593705, 0.998458288931568, 0.9986495525239756, 0.9988430656437745,
# 0.9990563718109147, 0.9992787669886709, 0.9994977021953116, 0.9997361622154102, 1.0]
print(matrix_cor(seq_tf_matrix(diff, 10)))
# 上证指数
# [-0.008651145443581408, 0.006834999895765309, 0.021172706952804994, -0.051951892446751614, 0.003523074227788529,
# 0.07219929582013276, 0.045227720273789414, -0.037241245123885554, 0.03602161724884085, 1.0]
# 标普500
# [-0.01350694261104616, -0.017031069396570293, -0.008112646905554516, -0.003528964957479074, -0.038046959576339454,
# -0.016926027633093563, 0.007025123013691551, -0.03670731870127638, -0.048211988418971646, 1.0]
