Mlp, Time Series Forecasting, Data Science, Machine Learning, TensorFlow.
for col in data.columns] # Target timestep (t=lag) cols.append(data.shift(-lag)) names += [('%s(t+%d)' % (col, lag)) for col in data.columns] # Put it all together agg = pd.concat(cols, axis=1) agg.columns = names # Drop rows with NaN values if dropnan: agg.dropna(inplace=True) return agg Kita akan