I have estimated the following two models:
Δy_t=0.015−0.410Δy_{t−1}−0.220Δy_{t−2}
and
Δyt=0.400+0.00145t−0.150y_{t−1}−0.325Δy_{t−1}−0.220Δy_{t−2}
(Note that yt is the log of monthly trading volume.)
How can I interpret how each is modelling the trend?
Related
All. I want to forecast sales, but the data is in the following format: different geo-locations have the same sales date, say I have three locations with three weeks of sales data, all three locations have the same dates. My question is whether I should create a unique N-BEATS model for each location.
It also has some categorical features, aside from one-hot, are there any other ways to use categorical features using the darts library like in PyTorch forecasting?
I have tried multiple GARCH model variations to remove financial time series characteristics from my dataset. I mainly tried ARMA(1,1),sGARCH models with normal distribution. My standardized residuals and squared standardized residuals don't show serial correlation anymore, which is good. However, the values for the goodness of fit test are always 0 or a very small number, which I think indicates that the model choice is not appropriate. What GARCH specification should I use?
(my dataset is a financial time series of daily relative spreads, so the spreads divided by the mean of ask en bid price of that day)
I have a dataset in which each line represents a person and their payment behavior during a full year. For each person I have 3 possible classification labels (age, gender, nationality). Payment behavior is defined by over 30 metrics such as number of payments and value of payments. Resulting dataset example looks something like this (I included a few random payment behavior metrics on the right):
My goal is to create classes (based on a combination of age/gender/nationality) that represent homogenous groups of people with similar payment behavior. For example: we find that 50-60 year old males from the US all have similar payment behavior. For each class I can then for example determine averages, standard deviations, percentiles etc. Since this seems to be an overlap between clustering and classification, I am stuck in what to research and where to look. Are there any methodologies I can look in to?
An option I'm thinking of would be to first create all possible classes (e.g. 50-M-US, 50-F-US, 51-M-US, etc.) and then merge them based on Euclidian distances (using all payment behavior metrics means) until a desired number of classes is left. Let me know what you think.
We are currently working on integrating ICD10-CM for our medical company, to be used for patient diagnosis. ICD10-CM is a coding system for diagnoses.
I tried to import ICD10-CM data in description-code pairs but obviously, it didn't work since AutoML needed more text for that code(label). I found a dataset on Kaggle but it only contained hrefs to an ICD10 website. I did find out that the website contains multiple texts and descriptions associated with codes that can be used to train our desired model.
Kaggle Dataset:
https://www.kaggle.com/shamssam/icd10datacom
Sample of a page from ICD10data.com:
https://www.icd10data.com/ICD10CM/Codes/A00-B99/A15-A19/A17-/A17.0
Most notable fields are:
- Approximate Synonyms
- Clinical Information
- Diagnosis Index
If I made a dataset from the sentences found in these pages and assigned them to their code(labels), will it be enough for AutoML dataset training? since each label will have 2 or more texts finally instead of just one, but definitely still a lot less than a 100 for each code unlike those in demos/tutorials.
From what I can see here, the disease code has a tree-like structure where, for instance, all L00-L99 codes refer to "Diseases of the skin and subcutaneous tissue". At the same time L00-L08 codes refer to "Infections of the skin and subcutaneous tissue", and so on.
What I mean is that the problem is not 90000 examples for 90000 different independent labels, but a decision tree (you take several decisions in function of the previous decision: the first step would be choosing which of the about 15 most general categories fits best, then choosing which of the subcategories etc.)
In this sense, probably autoML is not the best product, given that you cannot implement a specially designed decision tree model that takes into account all of this.
Another way of using autoML would be training separately for each of the decisions and then combine the different models. This would easily work for the first layer of decision but would be exponentially time consuming (the number of models to train in order to be able to predict more accurately grows exponentially with the level of accuracy, by accurate I mean afirminng it is L00-L08 instad of L00-L99).
I hope this helps you understand better the problem and the different approaches you can give to it!
I am building a RNN for a time series model, which have a categorical output.
For example, if precious 3 pattern is "A","B","A","B" model predict next is "A".
there's also a numerical level associated with each category.
For example A is 100, B is 50,
so A(100), B(50), A(100), B(50),
I have the model framework to predict next is "A", it would be nice to predict the (100) at the same time.
For real life examples, you have national weather data.
You are predicting the next few days weather type(Sunny, windy, raining ect...) at the same time, it would be nice model will also predict the temperature.
Or for Amazon, analysis customer's trxns pattern.
Customer A shopped category
electronic($100), household($10), ... ...
predict what next trxn category that this customer is likely to shop and predict at the same time what would be the amount of that trxns.
Researched a bit, have not found any relevant research on similar topics.
What is stopping you from adding an extra output to your model? You could have one categorical output and one numerical output next to each other. Every neural network library out there supports multiple outputs.
Your will need to normalise your output data though. Categories should be normalised with one-hot encoding and numerical values should be normalised by dividing by some maximal value.
Researched a bit, have not found any relevant research on similar topics.
Because this is not really a 'topic'. This is something completely normal, and it does not require some special kind of network.