Time series normalization using min max technique
$begingroup$
i have a time series dataset and i want to normalize the data(diff which is of type list) as below using Min Max technique but i got below error :
# split data into train and test-sets
train, test = diff[0:1486], diff[1486:2123]
from sklearn.preprocessing import MinMaxScaler
# scale train and test data to [-1, 1]
def scale(train, test):
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
train_scaled = scaler.transform(train)
# transform test
test = test.reshape(test.shape[0], test.shape[1])
test_scaled = scaler.transform(test)
return scaler, train_scaled, test_scaled
# transform the scale of the data
scaler, train_scaled, test_scaled = scale(train, test)
Error:
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
python
asked 14 mins ago
Rawia SammoutRawia Sammout
877
$endgroup$
add a comment |
$begingroup$
i have a time series dataset and i want to normalize the data(diff which is of type list) as below using Min Max technique but i got below error :
# split data into train and test-sets
train, test = diff[0:1486], diff[1486:2123]
from sklearn.preprocessing import MinMaxScaler
# scale train and test data to [-1, 1]
def scale(train, test):
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
train_scaled = scaler.transform(train)
# transform test
test = test.reshape(test.shape[0], test.shape[1])
test_scaled = scaler.transform(test)
return scaler, train_scaled, test_scaled
# transform the scale of the data
scaler, train_scaled, test_scaled = scale(train, test)
Error:
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
python
asked 14 mins ago
Rawia SammoutRawia Sammout
877
$endgroup$
add a comment |
$begingroup$
i have a time series dataset and i want to normalize the data(diff which is of type list) as below using Min Max technique but i got below error :
# split data into train and test-sets
train, test = diff[0:1486], diff[1486:2123]
from sklearn.preprocessing import MinMaxScaler
# scale train and test data to [-1, 1]
def scale(train, test):
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
train_scaled = scaler.transform(train)
# transform test
test = test.reshape(test.shape[0], test.shape[1])
test_scaled = scaler.transform(test)
return scaler, train_scaled, test_scaled
# transform the scale of the data
scaler, train_scaled, test_scaled = scale(train, test)
Error:
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
python
asked 14 mins ago
Rawia SammoutRawia Sammout
877
$endgroup$
i have a time series dataset and i want to normalize the data(diff which is of type list) as below using Min Max technique but i got below error :
# split data into train and test-sets
train, test = diff[0:1486], diff[1486:2123]
from sklearn.preprocessing import MinMaxScaler
# scale train and test data to [-1, 1]
def scale(train, test):
# fit scaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler = scaler.fit(train)
# transform train
train = train.reshape(train.shape[0], train.shape[1])
train_scaled = scaler.transform(train)
# transform test
test = test.reshape(test.shape[0], test.shape[1])
test_scaled = scaler.transform(test)
return scaler, train_scaled, test_scaled
# transform the scale of the data
scaler, train_scaled, test_scaled = scale(train, test)
Error:
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
python
python
asked 14 mins ago
Rawia SammoutRawia Sammout
877
asked 14 mins ago
Rawia SammoutRawia Sammout
877
edited 8 mins ago
Rawia Sammout
asked 14 mins ago
Rawia SammoutRawia Sammout
877
asked 14 mins ago
Rawia SammoutRawia Sammout
877
asked 14 mins ago
Rawia SammoutRawia Sammout
877
877
add a comment |
add a comment |
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