Exploring Uncertainty in Time Series Analysis

In summary, the conversation is about someone requesting help with a presentation on uncertainty in time series for an upcoming interview. They mention their engineering background and how they have been reading up on correlation, standard uncertainty, and linear regression analysis. Another person suggests using ARIMA models for calculating uncertainty in time series, and the original person asks if this approach can be applied to any time series data. The other person responds that it can be applied to any data, but is most useful for data with an autoregressive/moving average behavior. They also suggest using variance prediction from the ARIMA model for determining uncertainty. Finally, someone else offers to help based on their own engineering background.
  • #1
piper28
2
0
Hello all,

I am new to this forum and was wondering if any of you could help me out.

I have this interview scheduled for next week for which I have to prepare a 10 min presentation on Uncertainty and how to determine it in a time series. Now, there is a wealth of information of the net about uncertainty but nothing concrete when it comes to time series.

I am from an engineering background and have never studied uncertainty in any detail except for errors and measurement bias.
Also, this company is into measurement and data analysis and I am assuming these topics would be focussed towards measurement.

Could someone please point me in the right direction? does it have anything to do with correlation, standard uncertainty and linear regression analysis as I have been reading up on these for last couple of days.

Any help would be much appreciated.

Thanks.
 
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  • #3
Thank you viraltux for your prompt response. Just wondering, would this approach apply to any time series data acquired from any source?

I think I was just naive enough to assume that only knowledge of the source uncertainties would be required to determine them in time series.
 
  • #4
piper28 said:
Thank you viraltux for your prompt response. Just wondering, would this approach apply to any time series data acquired from any source?

I think I was just naive enough to assume that only knowledge of the source uncertainties would be required to determine them in time series.

You can apply it to any data regardless the source but, obviously, the ARIMA model becomes really useful if the data have an autoregressive / moving average behavior. Nonetheless, you can always use the variance prediction of the ARIMA model to determine the uncertainty of your time series.
 
  • #5
Piper what is your engineering background. Maybe i could help you.
 

1. What is uncertainty in time series?

Uncertainty in time series refers to the inherent unpredictability or variability in a set of data over time. It is often caused by external factors or random events that can affect the data points in a time series.

2. How is uncertainty measured in time series?

Uncertainty in time series is typically measured using statistical methods, such as calculating standard deviation or confidence intervals. These measures provide a range of values that the data points are likely to fall within, given the uncertainty in the data.

3. How does uncertainty impact the analysis of time series data?

Uncertainty can significantly affect the analysis of time series data, as it can make it more challenging to identify patterns or trends in the data. It can also make it difficult to make accurate predictions or forecasts based on the data.

4. What are some common sources of uncertainty in time series?

Some common sources of uncertainty in time series include measurement errors, incomplete or biased data, and external factors such as natural disasters or economic fluctuations. Additionally, human error or biases can also contribute to uncertainty in time series data.

5. How can uncertainty be managed in time series analysis?

While it is not possible to completely eliminate uncertainty in time series analysis, there are several ways to manage it. These include using robust statistical methods, incorporating multiple data sources, and conducting sensitivity analyses to assess the impact of uncertainty on the results.

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