(testing signal)

Tag: syntheticdata

Teaching AI to Classify Time-series Patterns with Synthetic Data – KDnuggets

What do we want to achieve?

 
 
We want to train an AI agent or model that can do something like this,

Image source: Prepared by the author using this Pixabay image (Free to use)

Variances, anomalies, shifts

 
 
Little more specifically, we want to train an AI agent (or model) to identify/classify time-series data for,

low/medium/high variance
anomaly frequencies (little or high fraction of anomalies)
anomaly scales (are the anomalies too far from the normal or close)
a positive or negative shift in the time-series data (in the presence of some anomalies)

But, we don’t want to complicate things

 
 
However, we don’t want to do a ton of feature engineering or learn complicated time-series algorithms (e.g.… Read more...

5 Development Rules to Improve Your Data Science Projects

1. Abstract scripts into functions and classes

Say you are working on a Jupyter notebook figuring out how to best visualize some data. As soon as that code works and you don’t think it will need much more debugging, it’s time to abstract it! Let’s look at an example,

import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import numpy as np
import pandas as pd

synthetic_data = np.random.normal(0,1,1000)

plt.plot(synthetic_data, color="green")
plt.title("Plotting Synthetic Data")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.show()

Here, we plotted some synthetic data. Assuming we are happy with our plot, what we want to do now is abstract this into a function and add it to the code base of our project:

def plotSyntheticDataTimeSeries(data):
plt.plot(data,
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Introducing the Synthetic Data Community

pip install ydata-synthetic
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Build a synthetic data pipeline using Gretel and Apache Airflow

By Drew Newberry, Software Engineer at Gretel.ai

Hey folks, my name is Drew, and I’m a software engineer here at Gretel. I’ve recently been thinking about patterns for integrating Gretel APIs into existing tools so that it’s easy to build data pipelines where security and customer privacy are first-class features, not just an afterthought or box to check.

One data engineering tool that is popular amongst Gretel engineers and customers is Apache Airflow. It also happens to work great with Gretel. In this blog post, we’ll show you how to build a synthetic data pipeline using Airflow, Gretel and PostgreSQL.

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Generating Synthetic Time-Series Data with Random Walks

While the data here is usable for time series models, no patterns are visible. Since actual data contains emergent patterns relationships to previous points, the synthetic data needs to be improved. Random walks are a viable solution to generate some realistic-looking behavior. Creating random walks in pandas requires iterating through each row of the dataframe. Each step in the walk depends on the previous step.

Below is the code to generate a random walk. The first ‘previous_value’ acts as the starting point for the walk. Next, the step size is set to 1. Finally, the ‘threshold’ sets the probability of walking positively or negatively to 50%.

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Synthetic Data Generation Using Conditional-GAN

In the world of information technology, companies use data to improve the customer experience and provide better services to their customers. Sometimes, the collection of data can be tedious and costly.

In this article, we will discuss GANs and specially Conditional GAN, a method we used for synthetic data generation at Y-Data. and how they can be used to generate synthetic datasets from them.

GAN was proposed by Ian Goodfellow et al.¹ in 2014 in this paper. The GAN architecture consists of two components called Generator and Discriminator. In simple words, the role of the generator is to generate new data (numbers, images, etc.)

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