[Ai competition series] new crown epidemic prediction-time series data prediction model

Related links:
[ai competition series] new crown epidemic prediction – BiLSTM future numerical prediction
[ai competition series] new crown epidemic prediction – stacked LSTM future numerical prediction
[ai competition series] new crown epidemic prediction – convolutional LSTM future numerical prediction

Participating in the competition for the first time to evaluate the future prediction of the new crown epidemic.
Add new data to the region’s history of new crowns for several months, and then predict the new crowns of the next week.
The official historical data is as follows:

The latter uses LSTM for prediction, and PyTorch realizes:

class LSTMpred(nn.Module):
    def __init__(self, input_size, hidden_dim):
        super(LSTMpred, self).__init__()
        self.input_dim = input_size
        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTM(input_size, hidden_dim)
        self.hidden2out = nn.Linear(hidden_dim, 1)
        self.hidden = self.init_hidden()

    def init_hidden(self):
        return (Variable(torch.zeros(1, 1, self.hidden_dim)),
                Variable(torch.zeros(1, 1, self.hidden_dim)))

    def forward(self, seq):
        lstm_out, self.hidden = self.lstm(
            seq.view(len(seq), 1, -1), self.hidden)
        outdat = self.hidden2out(lstm_out.view(len(seq), -1))
        return outdat

Then we input a time series, and then output one value or multiple values;
for this, the training set needs to be two time series data of training data and label;

# sep:原始数据, k:周期序列长度, gap:周期序列间隔
def trainDataGen_gap0(seq,k,gap):
    dat = list()
    L = len(seq)
    for i in range(L-k-gap):
        indat = list()
        outdat = list()
        count = gap+1
        for j in range(k):
            if count % (gap+1) == 0:
                indat.append(seq[i+j])
                outdat.append(seq[i+j+1+gap])
            count+= 1
        dat.append((indat,outdat))
    return dat

The simplest time series forecasting method is continuous data forecasting. For example, the first four values ​​are used to predict the next value, but this method is simple and linear in predicting future continuous data.
For example:
1~10 are known sequences, predict the future continuous data of 11-15, use the first four data to derive the next data:
7, 8, 9, 10 --> (11)
8, 9, 10 , (11) --> (12)
9, 10, (11), (12) --> (13)
10, (11), (12), (13) --> (14)
(11), (12), (13), (14) --> (15)
It is easy to see that more and more predictive data will be used as derivation data for the prediction of the following values, and the result will gradually become linear.
For example, a sequence derivation with an interval of 1:
3, 5, 7, 9 --> (11)
4, 6, 8, 10 --> (12)
5, 7, 9, (11) --> (13)
6, 8, 10, (12) --> (14)
7, 9, (11), (13) --> (15) In
this way, every future data derivation will involve more real data ;
And it may be possible to dig out the characteristics of some specific time intervals;

Finally, we train multiple models with multiple time series to get multiple results, and finally combine multiple results.

Complete code:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np
import get_data

### LSTM 实现时间序列预测

# 预设变量
xl_index = 3    # 读取xl文件的序列

continuous_seq_10_length = 10 #连续周期为10的序列长度
continuous_seq_5_length = 5 #连续周期为5的序列长度
continuous_seq_3_length = 3 #连续周期为3的序列长度
gap_1_seq_5_length = 3 #间隔为1周期为5的序列长度
gap_1_seq_9_length = 5 #间隔为1周期为9的序列长度
gap_2_seq_7_length = 3 #间隔为2周期为7的序列长度
gap_3_seq_9_length = 3 #间隔为3周期为9的序列长度
gap_6_seq_21_length = 3 #间隔一周的序列长度

def SeriesGen(N):
    x = torch.arange(1,N,0.01)
    print(len(x))
    return torch.sin(x)

# sep:原始数据, k:周期序列长度, gap:周期序列间隔
def trainDataGen_gap0(seq,k,gap):
    dat = list()
    L = len(seq)
    for i in range(L-k-gap):
        indat = list()
        outdat = list()
        count = gap+1
        for j in range(k):
            if count % (gap+1) == 0:
                indat.append(seq[i+j])
                outdat.append(seq[i+j+1+gap])
            count+= 1
        dat.append((indat,outdat))
    return dat

def ToVariable(x):
    tmp = torch.FloatTensor(x)
    return Variable(tmp)

class LSTMpred(nn.Module):
    def __init__(self, input_size, hidden_dim):
        super(LSTMpred, self).__init__()
        self.input_dim = input_size
        self.hidden_dim = hidden_dim
        self.lstm = nn.LSTM(input_size, hidden_dim)
        self.hidden2out = nn.Linear(hidden_dim, 1)
        self.hidden = self.init_hidden()

    def init_hidden(self):
        return (Variable(torch.zeros(1, 1, self.hidden_dim)),
                Variable(torch.zeros(1, 1, self.hidden_dim)))

    def forward(self, seq):
        lstm_out, self.hidden = self.lstm(
            seq.view(len(seq), 1, -1), self.hidden)
        outdat = self.hidden2out(lstm_out.view(len(seq), -1))
        return outdat


# 读取数据
case_count  = get_data.get_xldata(xl_index)
# 归一化处理
max_v = max(case_count)
new_case_count = [x/(max_v) for x in case_count]
# 生成序列文件
y = torch.tensor(new_case_count)
dat_10_0 = trainDataGen_gap0(y.numpy(),10,0)
dat_5_0 = trainDataGen_gap0(y.numpy(),5,0)
dat_3_0 = trainDataGen_gap0(y.numpy(),3,0)
dat_5_1 = trainDataGen_gap0(y.numpy(),5,1)
dat_9_1 = trainDataGen_gap0(y.numpy(),9,1)
dat_7_2 = trainDataGen_gap0(y.numpy(),7,2)
dat_9_3 = trainDataGen_gap0(y.numpy(),9,3)

# 模型训练参数设置
model_10_0 = LSTMpred(1,6) # 输入层和隐藏层尺寸
model_5_0 = LSTMpred(1,6) # 输入层和隐藏层尺寸
model_3_0 = LSTMpred(1,6) # 输入层和隐藏层尺寸
model_5_1 = LSTMpred(1,6) # 输入层和隐藏层尺寸
model_9_1 = LSTMpred(1,6) # 输入层和隐藏层尺寸
model_7_2 = LSTMpred(1,6) # 输入层和隐藏层尺寸
model_9_3 = LSTMpred(1,6) # 输入层和隐藏层尺寸

loss_function = nn.MSELoss() # 损失函数
optimizer_10_0 = optim.SGD(model_10_0.parameters(), lr=0.01) # 优化器设置，SGD方法，学习率0.01
optimizer_5_0 = optim.SGD(model_5_0.parameters(), lr=0.01) # 优化器设置，SGD方法，学习率0.01
optimizer_3_0 = optim.SGD(model_3_0.parameters(), lr=0.01) # 优化器设置，SGD方法，学习率0.01
optimizer_5_1 = optim.SGD(model_5_1.parameters(), lr=0.01) # 优化器设置，SGD方法，学习率0.01
optimizer_9_1 = optim.SGD(model_9_1.parameters(), lr=0.01) # 优化器设置，SGD方法，学习率0.01
optimizer_7_2 = optim.SGD(model_7_2.parameters(), lr=0.01) # 优化器设置，SGD方法，学习率0.01
optimizer_9_3 = optim.SGD(model_9_3.parameters(), lr=0.01) # 优化器设置，SGD方法，学习率0.01

num_epochs = 400 # 迭代次数

for epoch in range(num_epochs):
    print("迭代训练：",epoch)
    loss_sum = 0.0
    for seq, outs in dat_10_0[:]:
        seq = ToVariable(seq) # 转 tensor
        outs = ToVariable(outs)  # 转 tensor
        optimizer_10_0.zero_grad() # 清空梯度
        model_10_0.hidden = model_10_0.init_hidden()
        modout = model_10_0(seq) # 模型计算
        loss = loss_function(modout, outs) # 计算loss
        loss_sum += loss
        loss.backward()
        optimizer_10_0.step()
    # if epoch % 20 == 0 and epoch > 0:
    #     loss = loss_sum / len(dat)
    #     print("迭代训练",epoch,":",loss)

    for seq, outs in dat_5_0[:]:
        seq = ToVariable(seq) # 转 tensor
        outs = ToVariable(outs)  # 转 tensor
        optimizer_5_0.zero_grad() # 清空梯度
        model_5_0.hidden = model_5_0.init_hidden()
        modout = model_5_0(seq) # 模型计算
        loss = loss_function(modout, outs) # 计算loss
        loss_sum += loss
        loss.backward()
        optimizer_5_0.step()

    for seq, outs in dat_3_0[:]:
        seq = ToVariable(seq) # 转 tensor
        outs = ToVariable(outs)  # 转 tensor
        optimizer_3_0.zero_grad() # 清空梯度
        model_3_0.hidden = model_3_0.init_hidden()
        modout = model_3_0(seq) # 模型计算
        loss = loss_function(modout, outs) # 计算loss
        loss_sum += loss
        loss.backward()
        optimizer_3_0.step()

    for seq, outs in dat_5_1[:]:
        seq = ToVariable(seq) # 转 tensor
        outs = ToVariable(outs)  # 转 tensor
        optimizer_5_1.zero_grad() # 清空梯度
        model_5_1.hidden = model_5_1.init_hidden()
        modout = model_5_1(seq) # 模型计算
        loss = loss_function(modout, outs) # 计算loss
        loss_sum += loss
        loss.backward()
        optimizer_5_1.step()

    for seq, outs in dat_9_1[:]:
        seq = ToVariable(seq) # 转 tensor
        outs = ToVariable(outs)  # 转 tensor
        optimizer_9_1.zero_grad() # 清空梯度
        model_9_1.hidden = model_9_1.init_hidden()
        modout = model_9_1(seq) # 模型计算
        loss = loss_function(modout, outs) # 计算loss
        loss_sum += loss
        loss.backward()
        optimizer_9_1.step()

    for seq, outs in dat_7_2[:]:
        seq = ToVariable(seq) # 转 tensor
        outs = ToVariable(outs)  # 转 tensor
        optimizer_7_2.zero_grad() # 清空梯度
        model_7_2.hidden = model_7_2.init_hidden()
        modout = model_7_2(seq) # 模型计算
        loss = loss_function(modout, outs) # 计算loss
        loss_sum += loss
        loss.backward()
        optimizer_7_2.step()

    for seq, outs in dat_9_3[:]:
        seq = ToVariable(seq) # 转 tensor
        outs = ToVariable(outs)  # 转 tensor
        optimizer_9_3.zero_grad() # 清空梯度
        model_9_3.hidden = model_9_3.init_hidden()
        modout = model_9_3(seq) # 模型计算
        loss = loss_function(modout, outs) # 计算loss
        loss_sum += loss
        loss.backward()
        optimizer_9_3.step()





# 预测现有数据
predDat_10_0 = list()
for seq, trueVal in dat_10_0[:]:
    #print("seq=",seq)
    #print("trueval=",trueVal)
    seq = ToVariable(seq)
    trueVal = ToVariable(trueVal)
    modout = model_10_0(seq)
    pre = modout[-1].data.numpy()[0] #从 index = 5 开始
    #print(pre)
    loss = loss_function(modout, outs)
    loss_sum += loss
    predDat_10_0.append(model_10_0(seq)[-1].data.numpy()[0])

predDat_5_0 = list()
for seq, trueVal in dat_5_0[:]:
    seq = ToVariable(seq)
    trueVal = ToVariable(trueVal)
    modout = model_5_0(seq)
    pre = modout[-1].data.numpy()[0] #从 index = 5 开始
    loss = loss_function(modout, outs)
    loss_sum += loss
    predDat_5_0.append(model_5_0(seq)[-1].data.numpy()[0])

predDat_3_0 = list()
for seq, trueVal in dat_3_0[:]:
    seq = ToVariable(seq)
    trueVal = ToVariable(trueVal)
    modout = model_3_0(seq)
    pre = modout[-1].data.numpy()[0] #从 index = 5 开始
    loss = loss_function(modout, outs)
    loss_sum += loss
    predDat_3_0.append(model_3_0(seq)[-1].data.numpy()[0])

predDat_5_1 = list()
for seq, trueVal in dat_5_1[:]:
    seq = ToVariable(seq)
    trueVal = ToVariable(trueVal)
    modout = model_5_1(seq)
    pre = modout[-1].data.numpy()[0] #从 index = 5 开始
    loss = loss_function(modout, outs)
    loss_sum += loss
    predDat_5_1.append(model_5_1(seq)[-1].data.numpy()[0])

predDat_9_1 = list()
for seq, trueVal in dat_9_1[:]:
    seq = ToVariable(seq)
    trueVal = ToVariable(trueVal)
    modout = model_9_1(seq)
    pre = modout[-1].data.numpy()[0] #从 index = 5 开始
    loss = loss_function(modout, outs)
    loss_sum += loss
    predDat_9_1.append(model_9_1(seq)[-1].data.numpy()[0])

predDat_7_2 = list()
for seq, trueVal in dat_7_2[:]:
    seq = ToVariable(seq)
    trueVal = ToVariable(trueVal)
    modout = model_7_2(seq)
    pre = modout[-1].data.numpy()[0] #从 index = 5 开始
    loss = loss_function(modout, outs)
    loss_sum += loss
    predDat_7_2.append(model_7_2(seq)[-1].data.numpy()[0])

predDat_9_3 = list()
for seq, trueVal in dat_9_3[:]:
    seq = ToVariable(seq)
    trueVal = ToVariable(trueVal)
    modout = model_9_3(seq)
    pre = modout[-1].data.numpy()[0] #从 index = 5 开始
    loss = loss_function(modout, outs)
    loss_sum += loss
    predDat_9_3.append(model_9_3(seq)[-1].data.numpy()[0])


print("10_0:",len(dat_10_0),"; 5_0:",len(dat_5_0),"; 3_0:",len(dat_3_0),
      "; 5_1:",len(dat_5_1),"; 9_1:",len(dat_9_1),"; 7_2:",len(dat_7_2),"; 9_3:",len(dat_9_3))

# 制作测试标签
t_labels = y.numpy()[12:]
t_dat_10_0, t_dat_5_0 , t_dat_3_0, t_dat_5_1, t_dat_9_1, t_dat_7_2, t_dat_9_3 \
    = list(),list(),list(),list(),list(),list(),list()
#加载各个模型的预测值
for val,_ in dat_10_0[12-10:]:
    val = ToVariable(val)
    modout = model_10_0(val)
    pre = modout[-1].data.numpy()[0]
    t_dat_10_0.append(pre)
for val,_ in dat_5_0[12-5:]:
    val = ToVariable(val)
    modout = model_5_0(val)
    pre = modout[-1].data.numpy()[0]
    t_dat_5_0.append(pre)
for val,_ in dat_3_0[12-3:]:
    val = ToVariable(val)
    modout = model_3_0(val)
    pre = modout[-1].data.numpy()[0]
    t_dat_3_0.append(pre)
for val,_ in dat_5_1[12-6:]:
    val = ToVariable(val)
    modout = model_5_1(val)
    pre = modout[-1].data.numpy()[0]
    t_dat_5_1.append(pre)
for val, _ in dat_9_1[12 - 10:]:
    val = ToVariable(val)
    modout = model_9_1(val)
    pre = modout[-1].data.numpy()[0]
    t_dat_9_1.append(pre)
for val, _ in dat_7_2[12 - 9:]:
    val = ToVariable(val)
    modout = model_7_2(val)
    pre = modout[-1].data.numpy()[0]
    t_dat_7_2.append(pre)
for val, _ in dat_9_3[12 - 12:]:
    val = ToVariable(val)
    modout = model_9_3(val)
    pre = modout[-1].data.numpy()[0]
    t_dat_9_3.append(pre)

wl_10_0,wl_5_0,wl_3_0,wl_5_1,wl_9_1,wl_7_2,wl_9_3 = list(),list(),list(),list(),list(),list(),list()
X_list=list()
for i in range(len(t_dat_9_3)):
    # 求平均值
    # tmp = 0.0
    # tmp += t_dat_10_0[i]
    # tmp += t_dat_5_0[i]
    # tmp += t_dat_3_0[i]
    # tmp += t_dat_5_1[i]
    # tmp += t_dat_9_1[i]
    # tmp += t_dat_7_2[i]
    # tmp += t_dat_9_3[i]
    # X_list.append(tmp/7)
    # 根据误差获取权重
    loss_10_0 = 1/abs(t_dat_10_0[i] - t_labels[i])
    loss_5_0 = 1 / abs(t_dat_5_0[i] - t_labels[i])
    loss_3_0 = 1 / abs(t_dat_3_0[i] - t_labels[i])
    loss_5_1 = 1 / abs(t_dat_5_1[i] - t_labels[i])
    loss_9_1 = 1 / abs(t_dat_9_1[i] - t_labels[i])
    loss_7_2 = 1 / abs(t_dat_7_2[i] - t_labels[i])
    loss_9_3 = 1 / abs(t_dat_9_3[i] - t_labels[i])
    loss_sum = loss_10_0+loss_5_0+loss_3_0+loss_5_1+loss_9_1+loss_7_2+loss_9_3
    wl_10_0.append(loss_10_0/loss_sum)
    wl_5_0.append(loss_5_0/loss_sum)
    wl_3_0.append(loss_3_0/loss_sum)
    wl_5_1.append(loss_5_1/loss_sum)
    wl_9_1.append(loss_9_1/loss_sum)
    wl_7_2.append(loss_7_2/loss_sum)
    wl_9_3.append(loss_9_3/loss_sum)

# 求权重
w_10_0 = np.mean(wl_10_0)
w_5_0 = np.mean(wl_5_0)
w_3_0 = np.mean(wl_3_0)
w_5_1 = np.mean(wl_5_1)
w_9_1 = np.mean(wl_9_1)
w_7_2 = np.mean(wl_7_2)
w_9_3 = np.mean(wl_9_3)
w_sum = w_10_0+w_5_0+w_3_0+w_5_1+w_9_1+w_7_2+w_9_3
w_10_0 = w_10_0 / w_sum
w_5_0 = w_5_0 / w_sum
w_3_0 = w_3_0 / w_sum
w_5_1 = w_5_1 / w_sum
w_9_1 = w_9_1 / w_sum
w_7_2 = w_7_2 / w_sum
w_9_3 = w_9_3 / w_sum

# 预测现在值
for i in range(len(t_dat_9_3)):
    res = w_10_0 * t_dat_10_0[i] + w_5_0 * t_dat_5_0[i] + w_3_0 * t_dat_3_0[i] + w_5_1 * t_dat_5_1[i] + w_9_1 * t_dat_9_1[i] + w_7_2 * t_dat_7_2[i] + w_9_3 * t_dat_9_3[i]
    X_list.append(res)

#预测未来数据 --采用连续模型
#预测未来数据
pre_day_count = 10
seq,trueVal = dat_10_0[-1]
pred_10_0 = []
for i in range(pre_day_count):
    seq_val = ToVariable(seq)
    pre = model_10_0(seq_val)[-1].data.numpy()[0]
    pred_10_0.append(pre)
    seq=np.delete(seq, 0)
    seq=np.append(seq,pre)

seq,trueVal = dat_5_0[-1]
pred_5_0 = []
for i in range(pre_day_count):
    seq_val = ToVariable(seq)
    pre = model_5_0(seq_val)[-1].data.numpy()[0]
    pred_5_0.append(pre)
    seq=np.delete(seq, 0)
    seq=np.append(seq,pre)

seq,trueVal = dat_3_0[-1]
pred_3_0 = []
for i in range(pre_day_count):
    seq_val = ToVariable(seq)
    pre = model_3_0(seq_val)[-1].data.numpy()[0]
    pred_3_0.append(pre)
    seq=np.delete(seq, 0)
    seq=np.append(seq,pre)

# 开始间隔一的预测
seq_1,trueVal_1 = dat_5_1[-1]
seq,trueVal = dat_5_1[-2]
pred_5_1 = []
for i in range(pre_day_count):
    seq_val = ToVariable(seq)
    pre = model_5_1(seq_val)[-1].data.numpy()[0]
    pred_5_1.append(pre)
    seq = np.delete(seq, 0)
    seq = np.append(seq, pre)
    tmp1,tmp2 = seq_1 ,trueVal_1
    seq_1,trueVal_1 = seq,trueVal
    seq, trueVal = tmp1,tmp2

seq_1,trueVal_1 = dat_9_1[-1]
seq,trueVal = dat_9_1[-2]
pred_9_1 = []
for i in range(pre_day_count):
    seq_val = ToVariable(seq)
    pre = model_9_1(seq_val)[-1].data.numpy()[0]
    pred_9_1.append(pre)
    seq = np.delete(seq, 0)
    seq = np.append(seq, pre)
    tmp1,tmp2 = seq_1 ,trueVal_1
    seq_1,trueVal_1 = seq,trueVal
    seq, trueVal = tmp1,tmp2

# 开始间隔2的预测
seq_2,trueVal_2 = dat_7_2[-1]
seq_1,trueVal_1 = dat_7_2[-2]
seq,trueVal = dat_7_2[-3]
pred_7_2 = []
for i in range(pre_day_count):
    seq_val = ToVariable(seq)
    pre = model_7_2(seq_val)[-1].data.numpy()[0]
    pred_7_2.append(pre)
    seq = np.delete(seq, 0)
    seq = np.append(seq, pre)
    tmp1,tmp2 = seq_2,trueVal_2
    seq_2,trueVal_2 = seq_1,trueVal_1
    seq_1, trueVal_1 = seq, trueVal
    seq, trueVal = tmp1,tmp2

# 开始间隔3的预测
seq_3,trueVal_3 = dat_9_3[-1]
seq_2,trueVal_2 = dat_9_3[-2]
seq_1,trueVal_1 = dat_9_3[-3]
seq,trueVal = dat_9_3[-4]
pred_9_3 = []
for i in range(pre_day_count):
    seq_val = ToVariable(seq)
    pre = model_9_3(seq_val)[-1].data.numpy()[0]
    pred_9_3.append(pre)
    seq = np.delete(seq, 0)
    seq = np.append(seq, pre)
    tmp1,tmp2 = seq_3,trueVal_3
    seq_3, trueVal_3 = seq_2,trueVal_2
    seq_2,trueVal_2 = seq_1,trueVal_1
    seq_1, trueVal_1 = seq, trueVal
    seq, trueVal = tmp1,tmp2

final_pre = []
for i in range(pre_day_count):
    res = w_10_0*pred_10_0[i] + w_5_0*pred_5_0[i] + w_3_0*pred_3_0[i] + w_5_1*pred_5_1[i]+ w_9_1*pred_9_1[i] + w_7_2*pred_7_2[i] + w_9_3*pred_9_3[i]
    final_pre.append(res)
    X_list.append(res)
    print("pre:",res * max_v)


# fig = plt.figure()

true_y = []
true_pre= []
for val in y.tolist():
    val *= max_v
    true_y.append(val)
for val in X_list:
    val *= max_v
    true_pre.append(val)
# print(true_pre)
# 图像显示
plt.plot(true_y)
plt.plot(range(len(true_y)-len(true_pre)+pre_day_count-1, len(true_y)+pre_day_count-1), true_pre)
plt.show()