本文主要是用LSTM 前一个数据点的多变量预测下一个时间点的空气质量。
链接: https://pan.baidu.com/s/1G2uJVIYcRfRl_AOZFwlw6w 提取码: uf95
数据集的内容及其含义:
1.No 行数
2.year 年
3.month 月
4.day 日
5.hour 小时
6.pm2.5 PM2.5浓度
7.DEWP 露点
8.TEMP 温度
9.PRES 大气压
10.cbwd 风向
11.lws 风速
12.ls 累积雪量
13.lr 累积雨量
pollution dew temp press wnd_dir wnd_spd snow rain
date
2010-01-02 00:00:00 129.0 -16 -4.0 1020.0 SE 1.79 0 0
2010-01-02 01:00:00 148.0 -15 -4.0 1020.0 SE 2.68 0 0
2010-01-02 02:00:00 159.0 -11 -5.0 1021.0 SE 3.57 0 0
2010-01-02 03:00:00 181.0 -7 -5.0 1022.0 SE 5.36 1 0
2010-01-02 04:00:00 138.0 -7 -5.0 1022.0 SE 6.25 2 0
代码:
from pandas import read_csv
from datetime import datetime
# load data
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
# parse_dates参数: 将csv中的时间字符串转换成日期格式
dataset = read_csv('raw.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)
dataset.drop('No', axis=1, inplace=True)
# manually specify column names
dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
dataset.index.name = 'date'
# mark all NA values with 0
dataset['pollution'].fillna(0, inplace=True)
# drop the first 24 hours
dataset = dataset[24:]
# summarize first 5 rows
print(dataset.head(5))
# save to file
dataset.to_csv('pollution.csv')
加载“pollution.csv”文件,并对除了类别型特性“风速”的每一列数据分别绘图。
代码:
# load dataset
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
# specify columns to plot
groups = [0, 1, 2, 3, 5, 6, 7]
i = 1
# plot each column
pyplot.figure()
for group in groups:
pyplot.subplot(len(groups), 1, i)
pyplot.plot(values[:, group])
pyplot.title(dataset.columns[group], y=0.5, loc='right')
i += 1
pyplot.show()
# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(data) is list else data.shape[1]
df = DataFrame(data)
cols, names = list(), list()
# input sequence (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
if i == 0:
names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
# 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
# load dataset
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
# integer encode direction
encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])
# ensure all data is float
values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
# frame as supervised learning
reframed = series_to_supervised(scaled, 1, 1)
# drop columns we don't want to predict
reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True)
print(reframed.head())
打印转换后的数据集的前5行。我们可以看到8个输入变量(输入序列)和1个输出变量(当前小时的污染水平)
var1(t-1) var2(t-1) var3(t-1) var4(t-1) var5(t-1) var6(t-1) \
1 0.129779 0.352941 0.245902 0.527273 0.666667 0.002290
2 0.148893 0.367647 0.245902 0.527273 0.666667 0.003811
3 0.159960 0.426471 0.229508 0.545454 0.666667 0.005332
4 0.182093 0.485294 0.229508 0.563637 0.666667 0.008391
5 0.138833 0.485294 0.229508 0.563637 0.666667 0.009912
var7(t-1) var8(t-1) var1(t)
1 0.000000 0.0 0.148893
2 0.000000 0.0 0.159960
3 0.000000 0.0 0.182093
4 0.037037 0.0 0.138833
5 0.074074 0.0 0.109658
# split into train and test sets
values = reframed.values
n_train_hours = 365 * 24
train = values[:n_train_hours, :]
test = values[n_train_hours:, :]
# split into input and outputs
train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]
# reshape input to be 3D [samples, timesteps, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))
print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
(8760, 1, 8) (8760,) (35039, 1, 8) (35039,)
# design network
model = Sequential()
#model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
#model.add(Dense(50, activation='relu', input_dim = 8))
model.add(SimpleRNN(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')
# fit network
history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)
# plot history
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()
结果展示:
Epoch 1/50
122/122 - 1s - loss: 0.1125 - val_loss: 0.1060
Epoch 2/50
122/122 - 0s - loss: 0.0529 - val_loss: 0.1277
Epoch 3/50
122/122 - 0s - loss: 0.0395 - val_loss: 0.1122
Epoch 4/50
。。。。
# make a prediction
yhat = model.predict(test_X)
test_X = test_X.reshape((test_X.shape[0], test_X.shape[2]))
# invert scaling for forecast
inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]
# invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = concatenate((test_y, test_X[:, 1:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]
# calculate RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)
预测结果:
Test RMSE: 27.103