# Effective Time Series Forecasting using Keras/LSTM

I am working on time series forecasting for an engineering component (turbo charger). I have dataset containing field data from sensors (=features) taken every day for different turbocharger for their entire life (~3 years). I am using following combination LSTM, Keras, RNN.

One of the critical factor is efficiency of the component. Given that is make little sense to continue to use the charger once the efficiency has declined below threshold. I want to forecast the efficiency of turbocharger (in following time steps): 1 week, 5 week, 15 week, 30 week and 52 week from the date of forecasting.

My field data (shown in dummy, in reality I have about 13 features) looks like:

import pandas as pd
import random
import numpy
df = pd.DataFrame(pd.date_range(start="2019-09-01", end="2019-09-30", freq='D', name='ds'))
df["Charger_ID"]=1
df["DATE_INT_STAMP"] = range(1,31)
df["sensor1"] = np.random.randint(1, 30, df.shape[0])
df["sensor2"] = np.random.randint(55, 89, df.shape[0])
df["sensor3"] = np.random.randint(21, 35, df.shape[0])
df["efficiency"] = np.random.randint(71, 90, df.shape[0])