Split timeline for training LSTM network
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I have faced some trouble in splitting my dataset before feeding the data into an LSTM network. My data are time series, so that the only feasible way is to split them into training and testing part according to the time.
To do that I have selected a specific date that divide the timeline according to the desired proportion (70/30).
However, it is not the first time I get stuck with the choice of the timesteps in training an LSTM network with Keras.
My code does simply what follows:
split_date = '2002-03-01'
df_train = df[:split_date]
df_test = df[split_date:]
df_train.drop(df_train.tail(1).index,inplace=True)
Once I obtain the training and the test set, I find very difficult to resize the array as 3D shaped in order to provide the data to the LSTM. This is due to the fact that if I want to include 22 timesteps in my network, it happens that both sets could not be divisible for the number of timesteps. This implies that I am not able to resize them and I can't work with LSTM properly.
Is there a simple way to deal with timeline splitting, as in this case? Do the timesteps need to be the same for the training and testing part of an LSTM? Is is necessary to maintain this correspondence? In fact, this is the cause of my troble and it is not the first time I face this problem in working with LSTM for time series.
deep-learning keras dataset lstm
asked yesterday
AlexbriniAlexbrini
375
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add a comment |
$begingroup$
I have faced some trouble in splitting my dataset before feeding the data into an LSTM network. My data are time series, so that the only feasible way is to split them into training and testing part according to the time.
To do that I have selected a specific date that divide the timeline according to the desired proportion (70/30).
However, it is not the first time I get stuck with the choice of the timesteps in training an LSTM network with Keras.
My code does simply what follows:
split_date = '2002-03-01'
df_train = df[:split_date]
df_test = df[split_date:]
df_train.drop(df_train.tail(1).index,inplace=True)
Once I obtain the training and the test set, I find very difficult to resize the array as 3D shaped in order to provide the data to the LSTM. This is due to the fact that if I want to include 22 timesteps in my network, it happens that both sets could not be divisible for the number of timesteps. This implies that I am not able to resize them and I can't work with LSTM properly.
Is there a simple way to deal with timeline splitting, as in this case? Do the timesteps need to be the same for the training and testing part of an LSTM? Is is necessary to maintain this correspondence? In fact, this is the cause of my troble and it is not the first time I face this problem in working with LSTM for time series.
deep-learning keras dataset lstm
asked yesterday
AlexbriniAlexbrini
375
$endgroup$
add a comment |
$begingroup$
I have faced some trouble in splitting my dataset before feeding the data into an LSTM network. My data are time series, so that the only feasible way is to split them into training and testing part according to the time.
To do that I have selected a specific date that divide the timeline according to the desired proportion (70/30).
However, it is not the first time I get stuck with the choice of the timesteps in training an LSTM network with Keras.
My code does simply what follows:
split_date = '2002-03-01'
df_train = df[:split_date]
df_test = df[split_date:]
df_train.drop(df_train.tail(1).index,inplace=True)
Once I obtain the training and the test set, I find very difficult to resize the array as 3D shaped in order to provide the data to the LSTM. This is due to the fact that if I want to include 22 timesteps in my network, it happens that both sets could not be divisible for the number of timesteps. This implies that I am not able to resize them and I can't work with LSTM properly.
Is there a simple way to deal with timeline splitting, as in this case? Do the timesteps need to be the same for the training and testing part of an LSTM? Is is necessary to maintain this correspondence? In fact, this is the cause of my troble and it is not the first time I face this problem in working with LSTM for time series.
deep-learning keras dataset lstm
asked yesterday
AlexbriniAlexbrini
375
$endgroup$
I have faced some trouble in splitting my dataset before feeding the data into an LSTM network. My data are time series, so that the only feasible way is to split them into training and testing part according to the time.
To do that I have selected a specific date that divide the timeline according to the desired proportion (70/30).
However, it is not the first time I get stuck with the choice of the timesteps in training an LSTM network with Keras.
My code does simply what follows:
split_date = '2002-03-01'
df_train = df[:split_date]
df_test = df[split_date:]
df_train.drop(df_train.tail(1).index,inplace=True)
Once I obtain the training and the test set, I find very difficult to resize the array as 3D shaped in order to provide the data to the LSTM. This is due to the fact that if I want to include 22 timesteps in my network, it happens that both sets could not be divisible for the number of timesteps. This implies that I am not able to resize them and I can't work with LSTM properly.
Is there a simple way to deal with timeline splitting, as in this case? Do the timesteps need to be the same for the training and testing part of an LSTM? Is is necessary to maintain this correspondence? In fact, this is the cause of my troble and it is not the first time I face this problem in working with LSTM for time series.
deep-learning keras dataset lstm
deep-learning keras dataset lstm
asked yesterday
AlexbriniAlexbrini
375
asked yesterday
AlexbriniAlexbrini
375
asked yesterday
AlexbriniAlexbrini
375
asked yesterday
AlexbriniAlexbrini
375
asked yesterday
AlexbriniAlexbrini
375
375
add a comment |
add a comment |
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