Using 2 inputs time series to predict one output using LSTM

Venom_s: <p>I’m new to pytorch and LSTM, and I’m trying to follow a simple LSTM Time series prediction (<a href="https://stackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/" rel="nofollow noopener">https://stackabuse.com/time-series-prediction-using-lstm-with-pytorch-in-python/</a>) tutorial ,currently i’m using only one time series as input and it works fine, but i want to add another Time series and use it as a second input ( i want to use 2 features to predict one output) and i don’t know how to do it</p>
<pre><code class="lang-auto">import torch
import torch.nn as nn

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas import read_csv, read_excel
from sklearn.preprocessing import MinMaxScaler


fig_size = plt.rcParams["figure.figsize"]
fig_size[0] = 15
fig_size[1] = 5


#temp = read_csv('D:/downloads/t.csv')
#temp = temp.iloc[:, 0].values.astype(float)

all_pma = read_excel('D:/downloads/p.xlsx')
all_pma = all_pma.iloc[:, 0].values.astype(float)

#data = np.stack([all_pma, temp], 1)
#data = torch.tensor(data)

test_data_size = 24

train_data = all_pma[:-test_data_size]
test_data = all_pma[-test_data_size:]


scaler = MinMaxScaler(feature_range=(-1, 1))

train_data_normalized = scaler.fit_transform(train_data.reshape(-1, 1))   


train_data_normalized = torch.FloatTensor(train_data_normalized).view(-1)

#data_norm = torch.FloatTensor(data_norm).view(-1)

train_window = 24


def create_inout_sequences(input_data, tw):
    inout_seq = []
    L = len(input_data)
    for i in range(L - tw):
        train_seq = input_data[i:i + tw]
        train_label = input_data[i + tw:i + tw + 1]
        inout_seq.append((train_seq, train_label))
    return inout_seq


train_inout_seq = create_inout_sequences(train_data_normalized, train_window)


class LSTM(nn.Module):
    def __init__(self, input_size=1, hidden_layer_size=100, output_size=1):
        super().__init__()
        self.hidden_layer_size = hidden_layer_size

        self.lstm = nn.LSTM(input_size, hidden_layer_size)

        self.linear = nn.Linear(hidden_layer_size, output_size)

        self.hidden_cell = (torch.zeros(1, 1, self.hidden_layer_size),
                            torch.zeros(1, 1, self.hidden_layer_size))

    def forward(self, input_seq):
        lstm_out, self.hidden_cell = self.lstm(input_seq.view(len(input_seq), 1, -1), 
        self.hidden_cell)
        predictions = self.linear(lstm_out.view(len(input_seq), -1))
        return predictions[-1]


model = LSTM()
loss_function = nn.L1Loss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
print(model)

epochs = 1

for i in range(epochs):
    for seq, labels in train_inout_seq:
        optimizer.zero_grad()
        model.hidden_cell = (torch.zeros(1, 1, model.hidden_layer_size),
                             torch.zeros(1, 1, model.hidden_layer_size))

        y_pred = model(seq)

        single_loss = loss_function(y_pred, labels)
        single_loss.backward()
        optimizer.step()

    if i % 25 == 1:
        print(f'epoch: {i:3} loss: {single_loss.item():10.8f}')

print(f'epoch: {i:3} loss: {single_loss.item():10.10f}')

fut_pred = 24

test_inputs = train_data_normalized[-train_window:].tolist()
print(test_inputs)

model.eval()

for i in range(fut_pred):
    seq = torch.FloatTensor(test_inputs[-train_window:])
    with torch.no_grad():
        model.hidden = (torch.zeros(1, 1, model.hidden_layer_size),
                        torch.zeros(1, 1, model.hidden_layer_size))
        test_inputs.append(model(seq).item())

actual_predictions = scaler.inverse_transform(np.array(test_inputs[train_window:]).reshape(-1, 1))
print('actual predictions :', actual_predictions)

x = np.arange(8759, 8783, 1)

real_values = all_pma[8759:]
rv = np.array(real_values.reshape(-1, 1))



plt.title('Power Consumption')
plt.ylabel('Power Consumption')
plt.grid(True)
plt.autoscale(axis='x', tight=False)
plt.plot(x, rv, color='red')
plt.plot(x, actual_predictions, color='black')
plt.show()
</code></pre>