Can I apply different hyper-parameters for different sliding time windows?
Can I apply different hyper-parameters for different sliding time windows?
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Eiffelbear · External communityPost link
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Author: Eiffelbear
Original post: https://datascience.stackexchange.com/questions/89264
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Question
Can I apply different hyper-parameters for different training sets?
I can see the point of using the shared parameters but I cannot see the point of using shared hyper-parameters. The advantage of using the shared parameters is introduced in the following question:
Recurrent NNs: what's the point of parameter sharing? Doesn't padding do the trick anyway?
More Details
1) Explanations on My Model
As my model is a stock trading model, I will explain to you how I trade stocks. Please bear with me for explaining how I trade stocks. It is not a long explanation.
I am using
Bollinger bands
to trade stocks. (All the stock data in this example is
daily
).
In short, I calculate simple moving average (
SMA
) and standard deviation of N days long stock prices. (Yes, I assume that the stock prices follow the Gaussian distribution.) The
upper band
is
SMA + k*stdev
, while the
lower band
is
SMA - k*stdev
.
I buy the stock when the stock price is above the upper band ('Too_High_Buy') or below the lower band ('Too_Low_Buy'). For the 'Too_High_Buy' case, I sell the stock when the stock price goes below the SMA. For the 'Too_Low_Buy' case, I sell the stock when the stock price goes above the SMA.
The parameters of my model is
SMA
and
Stdev
, while the hyper-parameters are
N
and
k
.
N
: It decides how smooth SMA (the yellow line) will be.
k
: It decides how far the upper and lower bands will be located from the SMA.
As different values of N and K show different characteristics, we should search which values of N and K are good for stock price data.
2) How to decide the hyper-parameters(N & k)
I use '
sliding steps
' to decide the appropriate number for two hyper-parameters, N and K.
Sliding steps use the fixed amount of training data to decide the hyper parameter and check the performance of these hyper-parameters on the validation dataset, which immediately follows the training dataset.
I thought sliding steps is a good cross-validation tool to apply on stock data, because statistical properties of stocks can change while time goes by. For example, 30 years ago, McDonald and Coca-cola shows the similar price movements because they were sold together. However, nowadays, Coca-cola focuses on healthy drinks while McDonald stays as unhealthy food brands, they can show different price movements.
The hyper-parameter here can be several things, but for the sake of simplicity, let's say the hyper-parameters that we should decide is
the duration of training dataset (N)
. The duration of dropped and forecasting is set to 1 day.
Using grid-search of different values of N and K, I calculate which values of N and K shows the best performance during the validation period in the training dataset.
3) My question
Can I use different k for different training sets? In other words, can I dynamically change which value of K to use based on the performance of sub-training dataset?
While performing the grid-search in the sliding step window method, we use the same value of K in all the training datasets to trade a stock during the validation period.
However, we can use different values of K based on the performance of sub-training data.
For example, let's say N is fixed to be 30 days. Then from the 1st Jan to 30th Jan, k=0.6 shows the best performance and we use this k=0.6 for the 31st Jan. Then from the 2nd Jan to 31st Jan, k=1.5 shows the best performance, then we use this K value for the 1st Feb, and so on.
Why should we use the shared hyper-parameter K all across the model? For parameters, it makes sense because it allows reduction of the parameters that the model has to learn. (source:
Recurrent NNs: what's the point of parameter sharing? Doesn't padding do the trick anyway?
)
But for using shared hyper-parameters, the amount of hyper-parameters is the same whether I use the shared hyper-parameters or not. It is only 1 hyper-parameter, which is K. The amount of computations needed is the same as well.
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Brian Spiering · External communityPost link
External answer — Data Science Stack Exchange
Author: Brian Spiering
Original post: https://datascience.stackexchange.com/a/101796
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
Adaptation: HTML converted to plain text; contact email addresses removed.
Yes - different hyper-parameters for can be use for different training sets.
In your specific use case, by training on different time-based sliding windows you are modeling drift your time-series data. The different hyperparameters values will capture the gradual changes over time.
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Quoted from Forex.com.bd-Editorial External question — Data Science Stack Exchange Author: Eiffelbear Source score (net votes, not local likes): 1 Original post: https://datascience.stackexchange.com/questions/89264 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Question Can I apply different hyper-parameters for different training sets? I can see the point of using the shared parameters but I cannot see the point of using shared hyper-parameters. The advantage of using the shared parameters is introduced in the following question: Recurrent NNs: what's the point of parameter sharing? Doesn't padding do the trick anyway? More Details 1) Explanations on My Model As my model is a stock trading model, I will explain to you how I trade stocks. Please bear with me for explaining how I trade stocks. It is not a long explanation. I am using Bollinger bands to trade stocks. (All the stock data in this example is daily ). In short, I calculate simple moving average ( SMA ) and standard deviation of N days long stock prices. (Yes, I assume that the stock prices follow the Gaussian distribution.) The upper band is SMA + k*stdev , while the lower band is SMA - k*stdev . I buy the stock when the stock price is above the upper band ('Too_High_Buy') or below the lower band ('Too_Low_Buy'). For the 'Too_High_Buy' case, I sell the stock when the stock price goes below the SMA. For the 'Too_Low_Buy' case, I sell the stock when the stock price goes above the SMA. The parameters of my model is SMA and Stdev , while the hyper-parameters are N and k . N : It decides how smooth SMA (the yellow line) will be. k : It decides how far the upper and lower bands will be located from the SMA. As different values of N and K show different characteristics, we should search which values of N and K are good for stock price data. 2) How to decide the hyper-parameters(N & k) I use ' sliding steps ' to decide the appropriate number for two hyper-parameters, N and K. Sliding steps use the fixed amount of training data to decide the hyper parameter and check the performance of these hyper-parameters on the validation dataset, which immediately follows the training dataset. I thought sliding steps is a good cross-validation tool to apply on stock data, because statistical properties of stocks can change while time goes by. For example, 30 years ago, McDonald and Coca-cola shows the similar price movements because they were sold together. However, nowadays, Coca-cola focuses on healthy drinks while McDonald stays as unhealthy food brands, they can show different price movements. The hyper-parameter here can be several things, but for the sake of simplicity, let's say the hyper-parameters that we should decide is the duration of training dataset (N) . The duration of dropped and forecasting is set to 1 day. Using grid-search of different values of N and K, I calculate which values of N and K shows the best performance during the validation period in the training dataset. 3) My question Can I use different k for different training sets? In other words, can I dynamically change which value of K to use based on the performance of sub-training dataset? While performing the grid-search in the sliding step window method, we use the same value of K in all the training datasets to trade a stock during the validation period. However, we can use different values of K based on the performance of sub-training data. For example, let's say N is fixed to be 30 days. Then from the 1st Jan to 30th Jan, k=0.6 shows the best performance and we use this k=0.6 for the 31st Jan. Then from the 2nd Jan to 31st Jan, k=1.5 shows the best performance, then we use this K value for the 1st Feb, and so on. Why should we use the shared hyper-parameter K all across the model? For parameters, it makes sense because it allows reduction of the parameters that the model has to learn. (source: Recurrent NNs: what's the point of parameter sharing? Doesn't padding do the trick anyway? ) But for using shared hyper-parameters, the amount of hyper-parameters is the same whether I use the shared hyper-parameters or not. It is only 1 hyper-parameter, which is K. The amount of computations needed is the same as well.
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