LSTM Multi-state forecast
$begingroup$
I follow several tutorials about LSTM multi-step forecast to solve my problem.
My problem:
I have a time series of price about 3 months (Jan 2018 to Mar 2018) and I assume there are seasonal. So, my time series behaviors are changed because of such seasonal effects.
Q1
Do I need to apply sliding window to prevent learning from too long sequence. For example, each training sample takes prices of previous 14 days to predict the price of next 14 days. In other words, my input is (t-1), (t-2), ... , (t-7) and my output is (t), (t+1), ... , (t+6).
Q2
I read this post and feel that it's interesting by just passing a whole sequence and let the model makes decision by itself using the advantage of setting return_sequence=True and stateful=True. However, I'm a newbie to LSTM so I could not have clear understand how to apply it to the model according to such post.
Suppose my data look like this
import pandas as pd
import numpy as pd
np.random.seed(40)
df = pd.DataFrame({"Price":list(np.random.choice(range(1000, 1500), 14)) +
list(np.random.choice(range(1500, 2000), 14)) +
list(np.random.choice(range(1800, 2500), 14)) +
list(np.random.choice(range(1200, 1500), 14)) +
list(np.random.choice(range(1500, 2000), 14)) +
list(np.random.choice(range(1800, 2300), 14)) +
list(np.random.choice(range(2500, 3000), 14)) +
list(np.random.choice(range(2400, 2800), 14)) +
list(np.random.choice(range(2600, 3000), 14)) +
list(np.random.choice(range(2300, 2700), 14))},
index=pd.date_range(start="2018-03-25", periods=140))
df["Price"] = df["Price"].astype(float)
scaler = MinMaxScaler(feature_range=(0, 1))
df["Scaled"] = scaler.fit_transform(df["Price"].values.reshape(-1, 1))
If I configure my model as follows
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
model = Sequential()
model.add(LSTM(no_neuron, input_shape=(no_sample, timestep, no_feature)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
Is that make sense to set my timestep=40, no_sample=140, no_feature=1 for my problem? and How to define batch size?
Q3
In my settings, I really need to predict the price from Apr 2018 until Dec 2018 is that possible to do even I have only data from Jan 2018 to Mar 2018?
machine-learning deep-learning keras time-series lstm
asked 5 mins ago
Ratchainant ThammasudjaritRatchainant Thammasudjarit
101
New contributor
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I follow several tutorials about LSTM multi-step forecast to solve my problem.
My problem:
I have a time series of price about 3 months (Jan 2018 to Mar 2018) and I assume there are seasonal. So, my time series behaviors are changed because of such seasonal effects.
Q1
Do I need to apply sliding window to prevent learning from too long sequence. For example, each training sample takes prices of previous 14 days to predict the price of next 14 days. In other words, my input is (t-1), (t-2), ... , (t-7) and my output is (t), (t+1), ... , (t+6).
Q2
I read this post and feel that it's interesting by just passing a whole sequence and let the model makes decision by itself using the advantage of setting return_sequence=True and stateful=True. However, I'm a newbie to LSTM so I could not have clear understand how to apply it to the model according to such post.
Suppose my data look like this
import pandas as pd
import numpy as pd
np.random.seed(40)
df = pd.DataFrame({"Price":list(np.random.choice(range(1000, 1500), 14)) +
list(np.random.choice(range(1500, 2000), 14)) +
list(np.random.choice(range(1800, 2500), 14)) +
list(np.random.choice(range(1200, 1500), 14)) +
list(np.random.choice(range(1500, 2000), 14)) +
list(np.random.choice(range(1800, 2300), 14)) +
list(np.random.choice(range(2500, 3000), 14)) +
list(np.random.choice(range(2400, 2800), 14)) +
list(np.random.choice(range(2600, 3000), 14)) +
list(np.random.choice(range(2300, 2700), 14))},
index=pd.date_range(start="2018-03-25", periods=140))
df["Price"] = df["Price"].astype(float)
scaler = MinMaxScaler(feature_range=(0, 1))
df["Scaled"] = scaler.fit_transform(df["Price"].values.reshape(-1, 1))
If I configure my model as follows
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
model = Sequential()
model.add(LSTM(no_neuron, input_shape=(no_sample, timestep, no_feature)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
Is that make sense to set my timestep=40, no_sample=140, no_feature=1 for my problem? and How to define batch size?
Q3
In my settings, I really need to predict the price from Apr 2018 until Dec 2018 is that possible to do even I have only data from Jan 2018 to Mar 2018?
machine-learning deep-learning keras time-series lstm
asked 5 mins ago
Ratchainant ThammasudjaritRatchainant Thammasudjarit
101
New contributor
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I follow several tutorials about LSTM multi-step forecast to solve my problem.
My problem:
I have a time series of price about 3 months (Jan 2018 to Mar 2018) and I assume there are seasonal. So, my time series behaviors are changed because of such seasonal effects.
Q1
Do I need to apply sliding window to prevent learning from too long sequence. For example, each training sample takes prices of previous 14 days to predict the price of next 14 days. In other words, my input is (t-1), (t-2), ... , (t-7) and my output is (t), (t+1), ... , (t+6).
Q2
I read this post and feel that it's interesting by just passing a whole sequence and let the model makes decision by itself using the advantage of setting return_sequence=True and stateful=True. However, I'm a newbie to LSTM so I could not have clear understand how to apply it to the model according to such post.
Suppose my data look like this
import pandas as pd
import numpy as pd
np.random.seed(40)
df = pd.DataFrame({"Price":list(np.random.choice(range(1000, 1500), 14)) +
list(np.random.choice(range(1500, 2000), 14)) +
list(np.random.choice(range(1800, 2500), 14)) +
list(np.random.choice(range(1200, 1500), 14)) +
list(np.random.choice(range(1500, 2000), 14)) +
list(np.random.choice(range(1800, 2300), 14)) +
list(np.random.choice(range(2500, 3000), 14)) +
list(np.random.choice(range(2400, 2800), 14)) +
list(np.random.choice(range(2600, 3000), 14)) +
list(np.random.choice(range(2300, 2700), 14))},
index=pd.date_range(start="2018-03-25", periods=140))
df["Price"] = df["Price"].astype(float)
scaler = MinMaxScaler(feature_range=(0, 1))
df["Scaled"] = scaler.fit_transform(df["Price"].values.reshape(-1, 1))
If I configure my model as follows
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
model = Sequential()
model.add(LSTM(no_neuron, input_shape=(no_sample, timestep, no_feature)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
Is that make sense to set my timestep=40, no_sample=140, no_feature=1 for my problem? and How to define batch size?
Q3
In my settings, I really need to predict the price from Apr 2018 until Dec 2018 is that possible to do even I have only data from Jan 2018 to Mar 2018?
machine-learning deep-learning keras time-series lstm
asked 5 mins ago
Ratchainant ThammasudjaritRatchainant Thammasudjarit
101
New contributor
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I follow several tutorials about LSTM multi-step forecast to solve my problem.
My problem:
I have a time series of price about 3 months (Jan 2018 to Mar 2018) and I assume there are seasonal. So, my time series behaviors are changed because of such seasonal effects.
Q1
Do I need to apply sliding window to prevent learning from too long sequence. For example, each training sample takes prices of previous 14 days to predict the price of next 14 days. In other words, my input is (t-1), (t-2), ... , (t-7) and my output is (t), (t+1), ... , (t+6).
Q2
I read this post and feel that it's interesting by just passing a whole sequence and let the model makes decision by itself using the advantage of setting return_sequence=True and stateful=True. However, I'm a newbie to LSTM so I could not have clear understand how to apply it to the model according to such post.
Suppose my data look like this
import pandas as pd
import numpy as pd
np.random.seed(40)
df = pd.DataFrame({"Price":list(np.random.choice(range(1000, 1500), 14)) +
list(np.random.choice(range(1500, 2000), 14)) +
list(np.random.choice(range(1800, 2500), 14)) +
list(np.random.choice(range(1200, 1500), 14)) +
list(np.random.choice(range(1500, 2000), 14)) +
list(np.random.choice(range(1800, 2300), 14)) +
list(np.random.choice(range(2500, 3000), 14)) +
list(np.random.choice(range(2400, 2800), 14)) +
list(np.random.choice(range(2600, 3000), 14)) +
list(np.random.choice(range(2300, 2700), 14))},
index=pd.date_range(start="2018-03-25", periods=140))
df["Price"] = df["Price"].astype(float)
scaler = MinMaxScaler(feature_range=(0, 1))
df["Scaled"] = scaler.fit_transform(df["Price"].values.reshape(-1, 1))
If I configure my model as follows
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
model = Sequential()
model.add(LSTM(no_neuron, input_shape=(no_sample, timestep, no_feature)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
Is that make sense to set my timestep=40, no_sample=140, no_feature=1 for my problem? and How to define batch size?
Q3
In my settings, I really need to predict the price from Apr 2018 until Dec 2018 is that possible to do even I have only data from Jan 2018 to Mar 2018?
machine-learning deep-learning keras time-series lstm
machine-learning deep-learning keras time-series lstm
asked 5 mins ago
Ratchainant ThammasudjaritRatchainant Thammasudjarit
101
New contributor
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
Ratchainant ThammasudjaritRatchainant Thammasudjarit
101
New contributor
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
Ratchainant ThammasudjaritRatchainant Thammasudjarit
101
New contributor
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
Ratchainant ThammasudjaritRatchainant Thammasudjarit
101
asked 5 mins ago
Ratchainant ThammasudjaritRatchainant Thammasudjarit
101
101
New contributor
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Ratchainant Thammasudjarit is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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