Hi I am trying to understand how to interpret autocorrelation:
By looking at the graph how can we say the auto-correlation with the previous period (t + 1 in relation to t). TIA.
The following section from the Forecasting Principles and Practice book may help you to understand the interpretation of the Auto-correlation plots.
Thanks.
[1] https://otexts.com/fpp2/autocorrelation.html
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I'm working on a project that aims to find conflicting Semantic Sentences (NLP - Semantic Search )
For example
Our text is: "I ate today. The lunch was very tasty. I was an honest guest."
Query: "I had lunch with my friend"
Do we want to give the query model and find the meaning of the sentences with a certain point in terms of synonyms and antonyms?
The solution that came to my mind was to first find the synonymous sentences and extract the key words from the synonymous sentences and then get the semantic opposite words and then find the semantic synonymous sentences based on these opposite words.
Do you think this idea is possible? If you have a solution or experience in this area, please reply
Thanks
You have not mentioned the exact use case for your problem so I am not sure if the solution I know will help your cause. But there is an approach in NLP (using Deep learning) which helps to find whether two sentences are correlated, unrelated or contradictory.
Below is the information about the pretrained model which is trained specifically for this task ->
https://huggingface.co/facebook/bart-large-mnli
The dataset on which the above model is trained is given here ->
https://huggingface.co/datasets/glue/viewer/mnli/train
You can check the dataset to verify if your use case is related to the classification task performed on the dataset.
Since the model is already pretrained, you do not need to perform any training and can jump straight to evaluation. Once you can somewhat satisfied with the results, you can fine tune the model a bit for your specific problem.
We can talk in comments if you need more clarification.
can anyone please tell me how the author came to equation 2 from equation 1. I've applied Bayes rules but I'm not able to come to equation 2 directly. Thanks so much in advance.
These two are equal by definition of Bayes theorem :)
First, apologies in case the question is pretty basic.
Can anyone help me interpret the ACF/PACF plots to identify the values of AR and MA in ARIMA model?
My data set is network traffic in an office which means that it has seasonality of 168 points (hourly aggregation). This is because the traffic on all same days is similar (eg. All Monday's sees heavy traffic)
graph acf and pacf
If your data was non-stationary, the differenced ACF and PACF plots are the ones you should look at. Judging from the graphs you provided, the difference ACF shows a significant lag at 1 and it is positive in value, so consider adding AR(1) term to your model, that is for ARIMA, use p=1 and a q=0, because there is no significant negative correlation at lags 1 and above.
As per my understanding AR(p)=2 and MA(q)=1
AR(p)=2 and MA(q)=1
Please read this blog
https://arauto.readthedocs.io/en/latest/how_to_choose_terms.html
I'm studying the classification based on sparse coding and dictionary learning. I've read many documents but couldn't find an easy-to-understand one. As I understand, it's based on an optimization problem:
What's the meaning of the subscript (2)? And I guess the sign ||a|| means the amplitude of the vector?
And could you please suggest a good tutorial/introduction document for sparse coding? Thank you. (I tagged "image processing" and "machine learning" because I read somewhere that these fields use sparse coding. If not true, please comment and I'll remove the tags).
It's just definition of Mean-Squared Error
The subscript 2 means it's a l2 or Euclidean norm. See here for detailed explaination.
This is an high-level overview of sparse coding,
Is there a gem which enables you provide 3 to 4 values of known probability and generate en equation showing lognormal curve of best fit? The curve to be used for determining different values of (y) given the value of (x)
thanks Pierre
I didn't appreciate how hard this would be. A third party is providing javascript to go to solve this problem. Thanks - Pierre