Every day a new guru emerges with trade secrets for Black women who want to learn to be “feminine” in order to attract w
Data Columns: Any other columns that do not belong to the above categories will be considered data columns. The PAR class does not have an argument for assigning data columns. So, the remaining columns that are not listed in any of the previous three categories will automatically be considered data columns. In our example, the Noise column is the data column.
The SDV is capable of having multiple entities meaning multiple time-series. In our example, we have temperature measurements for multiple cities. In other words, each city has a group of measurements that will be treated independently.
SDV can model relational datasets by generating data after you specify the data schema using sdv.Metadata(). Moreover, you can plot the entity-relationship (ER) diagram by using the library built-in function. After the metadata is ready, new data can be generated using the Hierarchical Modeling Algorithm. You can find more information here.
We have Ciara to thank for the now-ubiquitous term “level up.” Her 2019 comeback song championed personal growth, while also being a thinly veiled reference to an Instagram post that implied single women did not have the right “spirit” for marriage. Today, much like the song, the “level up” movement blurs the boundaries between female empowerment and reproach, recycling a motley of regressive, superficial ideas about femininity taken from religious conservatism, celebrity culture, and the “trad” community, an offshoot of the misogynist “red pill” movement, which embraces traditional gender roles. A quick trip down the YouTube rabbit hole reveals titles like “femininity training,” “how to become high value,” “masculine traits to avoid,” and even “I changed my hair to become more feminine.”
Sequence Index: This is the data column with the row dependencies (should be sorted like datetime or numeric values). In time-series, this is usually the time axis. In our example, the sequence index will be the Date column.
Entity Columns: These columns are the abstract entities that form the group of measurements, where each group is a time-series (hence the rows within each group should be sorted). However, rows of different entities are independent of each other. In our example, the entity column(s) will be only the City column. By the way, we can have more columns as the argument type should be a list.
The PAR model for time series is implemented in PAR() class from sdv.timeseries module. If we want to model a single time-series data, then we only need to set the sequence_index argument of the PAR() class to the datetime column (the column illustrating the order of the time-series sequence). The magic happens in lines 8-16!
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n to fear or running away from it, why don’t you try to befriend fear? Establish a healthy relationship with your fear. Be open to the fact that it will always be there, no matter how far and fast you run away from it. The best you can do is draw clear boundaries and make it explicit that fear will get to have a say in your decisions, but it will not get to make those decisions for you. Your fear keeps your creativity in check.Black women are seeking love in a society that believes they are better suited for labor and sex, and we should be wary of advice that attempts to solve problems created by racism and misogyny by leaning more deeply into it.
When we encourage women to conform to a narrow, “one size fits all” feminine ideal rather than owning and developing who they are, we’re teaching women to dim the very qualities that make them interesting and attractive. More significantly, we send even louder messages to Black women that they are not enough, a belief that creates the very feelings of fear, guilt, and inadequacy that sabotage relationships, and primes women for exploitation. We don’t help women secure the bag by handing them even more emotional baggage to carry.
I get it. Some Black women have become so jaded in their pursuit of love, they’ve settled on the pursuit of money instead. And while I agree that Black women deserve to level up in every way imaginable, I’m wary that the ideas peddled in these hypergamous hives may be hindering them from doing just that.
Let’s work out an example to explain different arguments of PAR class. We are going to work with a time-series of temperatures in multiple cities. The dataset will have the following column: Date, City, Measuring Device, Where, Noise.
Perhaps the most glaring issue with telling Black women how to be feminine is the presumption that they are not. Much of the “level up” literature insidiously perpetuates racist, antebellum stereotypes that characterize Black womanhood as the antithesis of fragile, respectable, and chaste femininity — a status historically reserved for White women. Many women will stumble into this hypergamous community on the heels of heartbreak and romantic disillusionment, at their most vulnerable, only to buy into an ideology that presents lovelessness as the penalty for their failure to adhere to conventional femininity. Sure, connecting to your feminine intuition and sensuality can enhance your relationship with yourself and others, but the femininity glorified in these circles is a false, man-made construct designed to distract women from their authentic power. Black women are seeking love in a society that believes they are better suited for labor and sex, and we should be wary of advice that attempts to solve problems created by racism and misogyny by leaning more deeply into it.
A probabilistic autoregressive (PAR) model is used to model multi-type multivariate time-series data. The SDV library has this model implemented in the PAR class (from time-series module).
Context Columns: These columns provide information about the time-series’ entities and will not change over time. In other words, the context columns should be constant within groups. In our example, Measuring Device and Where are the context columns.