Investigating environmental time series and algorithms

In summary, the conversation is about investigating environmental time series and using algorithms to detect unexpected events. The speaker is constructing a gaussian probability density function and using spectral analysis to analyze periodicities in the data. They are seeking guidance on how to update the pdf according to the Fourier transform. The other speaker suggests cleaning the data from seasonal effects by dividing it by the norm of the seasonal effects, and explains how to do so in the case of a constant socket and fluctuations on top.
  • #1
wess80
1
0
Hi,

I am currently investigating environmental time series and algorithms to determine when an 'unexpected' event/reading has occurred in the series. I am currently constructing the gaussian probability density function (pdf) based on historical readings and checking if 'new' readings are acceptable according to a threshold in the pdf. As time goes by the pdf threshold becomes too large. Also, my time series contains daily and seasonal components.

I have received advice to use spectral analysis (using an FFT) on new month's data to compute the new month's periodicities. I have done so and now am faced with the question of how and what to do with the periodic frequencies in order to help determine what/when an unexpected reading is (according to the distribution).

I was thinking that perhaps it is possible to update a time series' pdf according to it's Fourier transform?

Could some one guide on this.

Thanks,

Wess
 
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  • #2
I can only tell what economists usually do. They clean the data from seasonal effects, i.e. they norm the data. If you know the seasonal (monthly) effects, you can divide the data by the norm of those effects. However, if e.g. the seasonal effects are of the form ##A+B(t)## and current data are ##C(t)##, i.e. a constant socket ##A## and fluctuations ##B(t)## on top, then you will only use ##B(t)## for normalization: ##C_0(t):= A+ \dfrac{C(t)-A}{B(t)}.##
 

1. What is the purpose of investigating environmental time series and algorithms?

The purpose of investigating environmental time series and algorithms is to understand and analyze patterns and trends in environmental data over time. This can help us make predictions, identify potential environmental issues, and inform decision-making processes.

2. What types of environmental data can be included in time series analysis?

Environmental time series analysis can include a wide range of data, such as temperature, precipitation, air quality, water quality, biodiversity, and land use. Any data that has been collected over a period of time can be used to create a time series.

3. How do algorithms play a role in environmental time series analysis?

Algorithms are used to process and analyze large amounts of environmental data in order to identify patterns and trends. They can also be used to make predictions and create models based on the data. Without algorithms, it would be difficult to efficiently analyze and make sense of complex time series data.

4. What are some challenges in investigating environmental time series?

Some challenges in investigating environmental time series include data quality issues, missing data, and dealing with complex and nonlinear relationships between variables. It can also be challenging to select the most appropriate algorithm for a specific dataset and to interpret the results in a meaningful way.

5. How is investigating environmental time series and algorithms beneficial?

Investigating environmental time series and algorithms can provide valuable insights into the state and changes of our environment. It can help us identify patterns and trends that may not be noticeable in short-term data, and can inform decision-making processes for environmental management and conservation. It can also contribute to the development of more accurate predictive models for future environmental conditions.

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