在 python 中使用 LSTM 添加滞后时的 NaN 值

Question

我正在尝试根据数据集分析和预测销售，我已经整理了我的数据，但是，当我尝试创建滞后时，每月销售滞后的值为 NaN，这个 NaN 是什么意思？从我所指的教程来看，他没有这些 NaN 值，至少当他丢弃 NaN 值时，他仍然有一些输出，但就我而言，当我丢弃 NaN 值时我什么都没有......

from __future__ import division
from datetime import datetime, timedelta, date
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

import warnings
warnings.filterwarnings("ignore")

import plotly.plotly as py
import plotly.offline as pyoff
import plotly.graph_objs as go

import keras
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import Adam 
from keras.callbacks import EarlyStopping
from keras.utils import np_utils
from keras.layers import LSTM
from sklearn.model_selection import KFold, cross_val_score, train_test_split

#initiate plotly
pyoff.init_notebook_mode()

#read data
df = pd.read_csv(r"C:\Users\User\Desktop\UOW\Yr3\FYP\Sample.csv", encoding='latin-1')

df['Order Date'] = pd.to_datetime(df['Order Date'])

df.head(10)

# Drop empty cells
df.dropna(axis=0, how='all', thresh=None, subset=None, inplace=False)
df.shape

# Drop unwanted columns
# Order ID, Ship Date, Ship Mode, Segment, Country, City, State, Postal Code, Region, Product ID, 
Category, Sub-Category, Product Name,

# Discount
df_sales = df.drop(['Order ID', 'Segment', 'Country', 'City', 'State', 'Postal Code', 'Region', 'Product ID', 'Category', 'Sub-Category', 'Product Name','Discount'], axis = 1)
df_sales.head(10)

# represent month in date field as its first day
df_sales['Order Date'] = pd.to_datetime(df_sales['Order Date']).dt.strftime("%Y-%m-%d")
df_sales = df_sales.groupby('Order Date').Sales.sum().reset_index()
df_sales

#plot monthly sales
 plot_data = [
    go.Scatter(
        x=df_sales['Order Date'],
        y=df_sales['Sales'],
    )
]
plot_layout = go.Layout(
         title='Montly Sales'
    )
fig = go.Figure(data=plot_data, layout=plot_layout)
pyoff.iplot(fig)

# Create a new dataframe to model the difference
df_diff = df_sales.copy()

# Add previous sales to the next row
df_diff['Prev_Sales'] = df_diff['Sales'].shift(1)

# Drop the null values and calculate the difference
df_diff = df_diff.dropna()
df_diff['diff'] = (df_diff['Sales'] - df_diff['Prev_Sales'])

df_diff.head(10)

#plot sales diff
plot_data = [
    go.Scatter(
        x=df_diff['Order Date'],
        y=df_diff['diff'],)]

plot_layout = go.Layout(
        title='Montly Sales Difference')

fig = go.Figure(data=plot_data, layout=plot_layout)
pyoff.iplot(fig)

#create dataframe for transformation from time series to supervised
df_supervised = df_diff.drop(['Prev_Sales'],axis=1)

#adding lags
for inc in range(1,13):
    field_name = 'lag_' + str(inc)
    df_supervised[field_name] = df_supervised['diff'].shift(inc)

#drop null values
#df_supervised = df_supervised.dropna().reset_index(drop=True)***

df_supervised

那么我得到的输出是

订购日期 |销售 |差异 |滞后_1 |滞后_2 |滞后_3 |滞后_4 |滞后_5 |滞后_6 | lag_7 |滞后_8 |滞后_9 | lag_10 | lag_11 | lag_12

1 2019-02-01 | 333904.9556 | -30136.6174 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠氮

2 2019-03-01 | 361431.8218 | 27526.8662 | -30136.6174 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠氮

3 2019-04-01 | 359930.1225 | -1501.6993 | 27526.8662 | -30136.6174 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠氮

4 2019-05-01 | 348999.4696 | -10930.6529 | -1501.6993 | 27526.8662 | -30136.6174 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠氮

5 2019-06-01 | 372904.5441 | 23905.0745 | -10930.6529 | -1501.6993 | 27526.8662 | -30136.6174 |钠 |钠 |钠 |钠 |钠 |钠 |钠 |钠氮

6 2019-07-01 | 372936.2013 | 31.6572 | 23905.0745 | -10930.6529 | -1501.6993 | 27526.8662 | -30136.6174 |钠 |钠 |钠 |钠 |钠 |钠 |钠氮

7 2019-08-01 | 328648.3505 | -44287.8508 | 31.6572 | 23905.0745 | -10930.6529 | -1501.6993 | 27526.8662 | -30136.6174 |钠 |钠 |钠 |钠 |钠 |钠氮

8 2019-09-01 | 371825.2898 | 43176.9393 | -44287.8508 | 31.6572 | 23905.0745 | -10930.6529 | -1501.6993 | 27526.8662 | -30136.6174 |钠 |钠 |钠 |钠氮

9 2019-10-01 | 363781.0459 | -8044.2439 | 43176.9393 | -44287.8508 | 31.6572 | 23905.0745 | -10930.6529 | -1501.6993 | 27526.8662 | -30136.6174 |钠
|钠 |钠 |钠氮

10 2019-11-01 | 336836.8240 | -26944.2219 | -8044.2439 | 43176.9393 | -44287.8508 | 31.6572 | 23905.0745 | -10930.6529 | -1501.6993 | 27526.8662 | -30136.6174 |钠 |钠 |钠氮

11 2019-12-01 | 374106.0722 | 37269.2482 | -26944.2219 | -8044.2439 | 43176.9393 | -44287.8508 | 31.6572 | 23905.0745 | -10930.6529 | -1501.6993 | 27526.8662 | -30136.6174 |钠 |钠氮

如果我取消注释此代码：df_supervised = df_supervised.dropna().reset_index(drop=True) 它只会显示标题的输出

订购日期 |销售 |差异 |滞后_1 |滞后_2 |滞后_3 |滞后_4 |滞后_5 |滞后_6 | lag_7 |滞后_8 |滞后_9 | lag_10 | lag_11 | lag_12

谁能帮我解决这个问题？非常感谢！

Answer 1

NaN 指的是非数字。

在使用滞后时间时通常会有一个 NaN。

如果您想保留数据，您应该尝试填充 NaN 而不是删除它们。

例如df.fillna(0)

您可以先看看这里：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html