Hi Sir, I have a 60000 rows of data that varies over time and I trie to fit an arx model to it but it doesn't work. I did the Preprocessing steps but still the error of the fit is so big
@@ikramelfatehy9804 Did you get a specific error message? If you want to share code snippets and more context, you can post on MATLAB Answers: www.mathworks.com/matlabcentral/answers/questions/new/
The way we store data is (needlessly, imo) flawed, tracing back to the pen-and-paper notations, as "Steve" failed to do. We often assume a time-series, and a constant sampling interval. There is no annotation, especially regarding timestamps, the source of the data, the manipulations (interpolation, filtering, etc) that may have been applied to the data, and statistical limitations inherent to the data itself (e.g. uncertainty). This can lead to major boo-boos. To give a concrete example - if a "smoothing" filter is used, as illustrated at 8:01, without annotation, it may lead to problems in that this filter is not a causal filter. The smoothed data may provide evidence of an event *before* the event happens. Without annotation, this shall cause headaches. Agreed that annotation would bloat data by several-X, but with our progressive (over)reliance on data, we ought to find a rigorous and standardized way to do this.
Let us know your follow-up data processing questions. I can connect you with the correct answers.
Hi Sir, I have a 60000 rows of data that varies over time and I trie to fit an arx model to it but it doesn't work. I did the Preprocessing steps but still the error of the fit is so big
@@ikramelfatehy9804 Did you get a specific error message? If you want to share code snippets and more context, you can post on MATLAB Answers: www.mathworks.com/matlabcentral/answers/questions/new/
The way we store data is (needlessly, imo) flawed, tracing back to the pen-and-paper notations, as "Steve" failed to do. We often assume a time-series, and a constant sampling interval. There is no annotation, especially regarding timestamps, the source of the data, the manipulations (interpolation, filtering, etc) that may have been applied to the data, and statistical limitations inherent to the data itself (e.g. uncertainty). This can lead to major boo-boos. To give a concrete example - if a "smoothing" filter is used, as illustrated at 8:01, without annotation, it may lead to problems in that this filter is not a causal filter. The smoothed data may provide evidence of an event *before* the event happens. Without annotation, this shall cause headaches.
Agreed that annotation would bloat data by several-X, but with our progressive (over)reliance on data, we ought to find a rigorous and standardized way to do this.
That’s why we filtfilt to avoid phase distortion:)
@@Afr0deeziac Filtfilt is not causal. You are aware of this, right?
I'd hate to have been "Steve".
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