Libardo1 · May 9, 2019 17:18 · Oct 29, 2017
diff --git a/README.md b/README.md
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+## REF
+https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/
+https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data
diff --git a/s0.py b/s0.py
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+from pandas import read_csv
+from datetime import datetime
+
+def parse(x):
+  return datetime.strptime(x, '%Y %m %d %H')
+
+dataset = read_csv('raw.csv',
+  parse_dates=[['year', 'month', 'day', 'hour',]],
+  index_col=0,
+  date_parser=parse)
+
+dataset.drop('No', axis=1, inplace=True)
+dataset.columns = ['pollution', 'dew', 'temp', 'press', 'wnd_dir', 'wnd_spd', 'snow', 'rain']
+dataset.index.name = 'date'
+dataset['pollution'].fillna(0, inplace=True)
+dataset = dataset[24:]
+print(dataset.head(5))
+dataset.to_csv('pollution.csv')
+
+"""
+from pandas import read_csv
+from datetime import datetime
+# load data
+def parse(x):
+  return datetime.strptime(x, '%Y %m %d %H')
+  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')
+"""
+# vim:ts=2 sts=2 ai sw=2 expandtab
diff --git a/s2.py b/s2.py
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+from pandas import DataFrame
+from pandas import concat
+
+def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
+  n_var = 1 if type(data) is list else data.shape[1]
+  df = DataFrame(data)
+  cols, names = list(), list()
+
+  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)]
+
+  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)]
+
+  agg = concat(cols, axis=1)
+  if dropnan:
+    agg.dropnan(inplace=True)
+  return dropnan
+
+dataset = read_csv('pollution.csv', header=0, index_col=0)
+values = dataset.values
+encoder = LabelEncoder()
+values[:,4] = encoder.fit_transform(values[:,4])
+values = values.astype('float32')
+scalar = MinMaxScalar(feature_range(0, 1))
+scaled = scalar.fit_transform(values)
+reframed = series_to_supervised(scaled, 1, 1)
+reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True)
+print(reframed.head(5))
+
+# vim: ts=2 sts=2 sw=2 expandtab ai
diff --git a/s3.py b/s3.py
@@ -0,0 +1,98 @@
+from math import sqrt
+from numpy import concatenate
+from matplotlib import pyplot
+from pandas import read_csv
+from pandas import DataFrame
+from pandas import concat
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import mean_squared_error
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM
+
+# 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 = 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)
+# specify the number of lag hours
+n_hours = 3
+n_features = 8
+# frame as supervised learning
+reframed = series_to_supervised(scaled, n_hours, 1)
+print(reframed.shape)
+
+# 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
+n_obs = n_hours * n_features
+train_X, train_y = train[:, :n_obs], train[:, -n_features]
+test_X, test_y = test[:, :n_obs], test[:, -n_features]
+print(train_X.shape, len(train_X), train_y.shape)
+# reshape input to be 3D [samples, timesteps, features]
+train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))
+test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))
+print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
+
+# design network
+model = Sequential()
+model.add(LSTM(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()
+
+# make a prediction
+yhat = model.predict(test_X)
+test_X = test_X.reshape((test_X.shape[0], n_hours*n_features))
+# invert scaling for forecast
+inv_yhat = concatenate((yhat, test_X[:, -7:]), 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[:, -7:]), 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)
diff --git a/s4.py b/s4.py
@@ -0,0 +1,98 @@
+from math import sqrt
+from numpy import concatenate
+from matplotlib import pyplot
+from pandas import read_csv
+from pandas import DataFrame
+from pandas import concat
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import mean_squared_error
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM
+
+# 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 = 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)
+# specify the number of lag hours
+n_hours = 3
+n_features = 8
+# frame as supervised learning
+reframed = series_to_supervised(scaled, n_hours, 1)
+print(reframed.shape)
+
+# 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
+n_obs = n_hours * n_features
+train_X, train_y = train[:, :n_obs], train[:, -n_features]
+test_X, test_y = test[:, :n_obs], test[:, -n_features]
+print(train_X.shape, len(train_X), train_y.shape)
+# reshape input to be 3D [samples, timesteps, features]
+train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))
+test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))
+print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)
+
+# design network
+model = Sequential()
+model.add(LSTM(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()
+
+# make a prediction
+yhat = model.predict(test_X)
+test_X = test_X.reshape((test_X.shape[0], n_hours*n_features))
+# invert scaling for forecast
+inv_yhat = concatenate((yhat, test_X[:, -7:]), 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[:, -7:]), 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)