Smoother EWMA that mean-reverts

  • I
  • Thread starter cppIStough
  • Start date
In summary, "Smoother EWMA that mean-reverts" discusses an enhanced Exponentially Weighted Moving Average (EWMA) model that incorporates a mean-reversion mechanism. This approach aims to reduce noise in time series data while ensuring that the estimates return to a long-term average over time. The model is particularly useful in financial applications where trends and fluctuations need to be analyzed more accurately, allowing for better forecasting and decision-making.
  • #1
cppIStough
22
2
EWMA (exponential weighted moving average) is one way to estimate variance of time series data, and is pretty well known. The issue I have with EWMA is the maximums aren't smooth, especially when recovering from a time-series large spike, and it can take a little while to recover to pre-spike levels. I'm wondering if you know of (or are creative enough to come up with it yourself) a smoother EWMA that reverts to previous-spike levels quicker.

Let me know if I'm not clear, and thanks again for your advice!
 
Physics news on Phys.org
  • #2
You might consider a fixed-time moving average. The data of Covid-19 deaths is a good example. That data is often presented with 3-day and 7-day moving average options. The 7-day MA has an advantage of always including one weekend, when reporting is always low, and a Monday/Tuesday, when the reports catch up for the weekend (either this weekend or the prior weekend). The advantage is that it greatly smooths out the daily average numbers and suppresses the weekly cycles. The disadvantage is that any spike or variation is watered down by the surrounding 6 days.

Alternatively, you could use your own weights.
 
Last edited:
  • Like
Likes fresh_42
  • #3
Covid was an interesting example @FactChecker mentioned. It shows the questions I had (and didn't post as they missed rigor until I saw the Covid example).

What is a spike, a potential data error (random), or a system immanent error (repeated) as in the Covid case? Is there a specific point above which you call data a spike? The word spike has a connotation of something you see in the data, not of something you measure. You first have to make it measurable in order to deal with it.
 
  • Like
Likes FactChecker
  • #4
fresh_42 said:
Covid was an interesting example @FactChecker mentioned. It shows the questions I had (and didn't post as they missed rigor until I saw the Covid example).

What is a spike, a potential data error (random), or a system immanent error (repeated) as in the Covid case? Is there a specific point above which you call data a spike? The word spike has a connotation of something you see in the data, not of something you measure. You first have to make it measurable in order to deal with it.
Those are the big questions: What do you measure, how do you measure it? Maybe too many only deal with technical aspects but don't dwell on such important questions.
 
  • Like
Likes FactChecker

FAQ: Smoother EWMA that mean-reverts

What is a Smoother EWMA that mean-reverts?

A Smoother Exponentially Weighted Moving Average (EWMA) that mean-reverts is a statistical technique used to smooth time series data while incorporating the tendency of the data to revert to a long-term mean. This combines the benefits of EWMA, which gives more weight to recent observations, with mean-reversion properties, making it useful for various applications in finance and signal processing.

How does a Smoother EWMA differ from a standard EWMA?

A standard EWMA applies exponentially decreasing weights to past observations, emphasizing recent data points. In contrast, a Smoother EWMA that mean-reverts not only applies these weights but also incorporates a mechanism to pull the moving average back towards a long-term mean, making it more stable and less sensitive to short-term fluctuations.

What are the applications of a Smoother EWMA that mean-reverts?

This technique is widely used in financial markets for volatility forecasting, risk management, and algorithmic trading. It is also applicable in other fields such as engineering, where it can be used for noise reduction in signal processing, and in environmental science for smoothing and predicting climate data.

How do you calculate a Smoother EWMA that mean-reverts?

The calculation involves two main steps: first, compute the standard EWMA using a chosen smoothing parameter (lambda); second, adjust this EWMA towards the long-term mean using a mean-reversion parameter. The specific formula may vary, but it typically involves a weighted combination of the current EWMA value and the long-term mean.

What are the advantages of using a Smoother EWMA that mean-reverts?

The main advantages include improved stability and reduced sensitivity to short-term volatility, making it more reliable for long-term forecasting and analysis. It also helps in capturing the underlying trend more accurately by accounting for the mean-reverting nature of many real-world time series.

Similar threads

I Maximum likelihood to fit a parameter of this model
Replies
1
Views
1K
How Should I Set the Time Constant for LPF/EWMA?
Replies
1
Views
3K
I Bayes again - how to interpret in vitro genotoxicity data
Replies
8
Views
2K
Studying I have some epic math goals and creating a study plan
Replies
11
Views
2K
Introductory Time Series Analysis Textbook
Replies
2
Views
3K
I Progress In Calculating The HLbL Contribution To Muon g-2
Replies
0
Views
282
I Cyclotron resonance and Landau Levels
Replies
4
Views
3K
Von Karman curve fitting to field measured spectrum
Replies
1
Views
2K
Transfer Student-> Engineering/Physics Program
Replies
2
Views
2K
Structure of the Galaxy: 6 Questions Confirmed
Replies
31
Views
3K

Hot Threads

Recent Insights

Back
Top