<gh_stars>10-100
# Store "window" points as a sequence
xin = []
next_X1 = []
for i in range(window,len(Xtest)):
    xin.append(Xtest[i-window:i])
    next_X1.append(Xtest[i])

# Reshape data to format for LSTM
xin, next_X1 = np.array(xin), np.array(next_X1)
xin = xin.reshape((xin.shape[0], xin.shape[1], 1))

# Predict the next value (1 step ahead)
X_pred = m.predict(xin)

# Plot prediction vs actual for test data
plt.figure()
plt.plot(X_pred,':',label='LSTM')
plt.plot(next_X1,'--',label='Actual')
plt.legend()
# Store "window" points as a sequence
xin = []
next_X1 = []
for i in range(window,len(Xtest)):
    xin.append(Xtest[i-window:i])
    next_X1.append(Xtest[i])

# Reshape data to format for LSTM
xin, next_X1 = np.array(xin), np.array(next_X1)
xin = xin.reshape((xin.shape[0], xin.shape[1], 1))

# Predict the next value (1 step ahead)
X_pred = m.predict(xin)

# Plot prediction vs actual for test data
plt.figure()
plt.plot(X_pred,':',label='LSTM')
plt.plot(next_X1,'--',label='Actual')
plt.legend()
# Store "window" points as a sequence # Reshape data to format for LSTM # Predict the next value (1 step ahead) # Plot prediction vs actual for test data