Predicting X_t as X_{t-1} Gives MSE 0.07

[burakbayramli]

Thanks for making your LSTM time series code available; it reported MSE of 0.07. I tried to create a baseline for comparison, simply taking X_t as X_{t-1}. This also gives me MSE of 0.07. Is this odd, or maybe LSTM generalizes better, or my MSE computation is faulty?

Thanks,

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_csv('household_power_consumption.txt', sep=';')
df = df[['Global_active_power']]
df = df[df.Global_active_power != '?']
df['G2'] = df['Global_active_power'].shift(1)
print df.head()
df = df.astype(float)
df['err'] = df['G2']-df['Global_active_power']
df['err'] = np.power(df['err'],2)
print df.err.sum() / len(df)
#print np.sqrt(df['err'].sum()) / len(df)