# Temporian

Published articles for Temporian.

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## Pre-processing temporal data made easier with TensorFlow Decision Forests and Temporian

DevFeed: [Pre-processing temporal data made easier with TensorFlow Decision Forests and Temporian](<https://devfeed.tech/articles/pre-processing-temporal-data-made-easier-with-tensorflow-decision-forests-and-temporian-7387.md>)

Original publisher: [Read original article](<https://blog.tensorflow.org/2023/09/forecasting-with-tensorflow-decision-forests-and-temporian.html>)

Author: TensorFlow Blog (noreply@blogger.com)

Published: 2023-09-11T20:14:00Z

Content type: article

Language: en

Sources: [The TensorFlow Blog](<https://devfeed.tech/sources/the-tensorflow-blog.md>)

Topics: [Temporal data](<https://devfeed.tech/topics/temporal-data.md>), [Temporian](<https://devfeed.tech/topics/temporian.md>), [TensorFlow Decision Forests](<https://devfeed.tech/topics/tensorflow-decision-forests.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [Python](<https://devfeed.tech/topics/python.md>), [datasets](<https://devfeed.tech/topics/datasets.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [data](<https://devfeed.tech/tags/data.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [logs](<https://devfeed.tech/tags/logs.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [python](<https://devfeed.tech/tags/python.md>), [sales](<https://devfeed.tech/tags/sales.md>), [temporal-data](<https://devfeed.tech/tags/temporal-data.md>), [temporian](<https://devfeed.tech/tags/temporian.md>), [tensorflow-decision-forests](<https://devfeed.tech/tags/tensorflow-decision-forests.md>), [time-sequences](<https://devfeed.tech/tags/time-sequences.md>), [time-series](<https://devfeed.tech/tags/time-series.md>), [transactions](<https://devfeed.tech/tags/transactions.md>), [user-event](<https://devfeed.tech/tags/user-event.md>)

### AI overview

This article demonstrates preprocessing temporal sales data with Temporian and forecasting weekly sales with TensorFlow Decision Forests. It explains event sets, aggregation, moving sums, indexing, and the trade-off between transactional detail and uniformly sampled time series.

### Source excerpt

Posted by Google: Mathieu Guillame-Bert, Richard Stotz, Robert Crowe, Luiz GUStavo Martins (Gus), Ashley Oldacre, Kris Tonthat, Glenn Cameron, and Tryolabs: Ian Spektor, Braulio Rios, Guillermo Etchebarne, Diego Marvid, Lucas Micol, Gonzalo Marín, Alan Descoins, Agustina Pizarro, Lucía Aguilar, Martin Alcala Rubi Temporal data is omnipresent in applied machine learning applications. Data often changes over time or is only available or valuable at a certain point in time. For example, market prices and weather conditions change constantly. Temporal data is also often highly discriminative in decision-making tasks. For example, the rate of change and interval between two consecutive heartbeats provides valuable insights into a person's physical health, and temporal patterns of network logs are used to detect configuration issues and intrusions. Hence, it is essential to incorporate temporal data and temporal information in ML applications. INFO: Temporian is a new open-source Python library for preprocessing and feature engineering temporal data for machine learning applications. It is developed in collaboration between Google and Tryolabs. Check the sister blog post for more details. This blog post demonstrates how to train a forecasting model on transactional data. Specifically, we will show how to forecast the total weekly sales from individual sales records. For the modeling part, we will use TensorFlow Decision Forests as they are well suited to handle temporal data. To feed the transaction data to our model, and to compute temporal specific features, we will use Temporian, a newly released library designed for ingesting and aggregating transactional data from multiple non-synchronized sources. Time series are the most commonly used representation for temporal data. They consist of uniformly sampled values, which can be useful for representing aggregate signals. However, time series are sometimes not sufficient to represent the richness of available data. Instead