Entity Columns: These columns are the abstract entities that form the group of measurements, where each group is a time-

The statues of Antinous all share similar features, making them relatively easy to identify. As the Roman Empire moved away from paganism and towards Christianity, worshiping deities was forbidden. Many of the statues were destroyed over the centuries that followed. Still, about eighty sculptures have survived to this day, showing Antinous with a swollen chest, a slightly lowered face, and fabulous Greek curls. It was Hadrian’s love for him and his features that have surpassed the test of time.

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.

The Synthetic Data Vault (SDV) was first introduced in the paper “The Synthetic data vault”, then used in the context of generative modeling in the master thesis “The Synthetic Data Vault: Generative Modeling for Relational Databases” by Neha Patki. Finally, the SDV library was developed as a part of Andrew Montanez’s master thesis “SDV: An Open Source Library for Synthetic Data Generation”. Another master thesis to add new features to SDV was done by Lei Xu (“Synthesizing Tabular Data using conditional GAN”).

The workflow of this library is shown below. A user provides the data and the schema and then fits a model to the data. At last, new synthetic data is obtained from the fitted model [2]. Moreover, the SDV library allows the user to save a fitted model (model.save('model.pkl')) for any future use.

The reason I’m bringing the history of the SDV is to appreciate the amount of work and research that has gone behind this library. An interesting article talking about the potential of using this tool, particularly in data privacy is available here.

Antinous’s death in the Nile played a central role in the mythology Hadrian built around his godliness. In Egyptian mythology, his death could also mean that the river god had taken him and made him a deity. Because many Egyptians already saw the young man as a deity without Hadrian having to proclaim this, a cult could be built up and cultivated around him. Hadrian used this fact to declare his Antinous a god in October 130. This quickly met with approval, and in many places, temples were built in his honor. It is also said that Hadrian commissioned up to 2000 statues.

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ch to self-love is centred around understanding your worth and not settling for anything less. The writing is compassionate and compelling, forcing you to rethink several of your core beliefs. Sincero’s self-love rule follows the following strategy:

The statues of Antinous all share similar features, making them relatively easy to identify. As the Roman Empire moved away from paganism and towards Christianity, worshiping deities was forbidden. Many of the statues were destroyed over the centuries that followed. Still, about eighty sculptures have survived to this day, showing Antinous with a swollen chest, a slightly lowered face, and fabulous Greek curls. It was Hadrian’s love for him and his features that have surpassed the test of time.

In data science, you usually need a realistic dataset to test your proof of concept. Creating fake data that captures the behavior of the actual data may sometimes be a rather tricky task. Several python packages try to achieve this task. Few popular python packages are Faker, Mimesis. However, there are mostly generating simple data like generating names, addresses, emails, etc.

The main reason I’m interested in this tool is for system testing: It’s much better to have datasets that are generated from the same actual underlying process. This way we can test our work/model in a realistic scenario rather than having unrealistic cases. There are other reasons why we need synthetic data such as data understanding, data compression, data augmentation, and data privacy [1].

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).

All these work and research were done in the MIT Data-to-AI laboratory under the supervision of Kalyan Veeramachaneni — a principal research scientist at MIT Laboratory for Information and Decision Systems (LIDS, MIT).

To say Hadrian’s reaction to the death of his boyfriend was heart-broken wouldn’t give all of his efforts for the world to remember his real love credit. After reportedly breaking down and weeping publicly, he ordered a city to be built in his honor and named a star after his great love. But that is not all! To really pay tribute to his lover, Hadrian made him a god.

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.

To create data that captures the attributes of a complex dataset, like having time-series that somehow capture the actual data’s statistical properties, we will need a tool that generates data using different approaches. Synthetic Data Vault (SDV) python library is a tool that models complex datasets using statistical and machine learning models. This tool can be a great new tool in the toolbox of anyone who works with data and modeling.