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Time Series with data.table in R
Master time series data using data.table in R.
- 4 hours
- 14 Videos
- 52 Exercises
- 1,055 Participants
- 4,200 XP
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Course Description
Time series data is fun, but challenging. When ordering matters, your datasets get large, and timestamp precision differences can foil your merges, building reliable data processing pipelines requires a principled approach with the right tools. Enter data.table! Its expressive syntax will make your code powerful without sacrificing readability and its support for in-place operations will make your code super fast. Learn how to master time series data in data.table with this course!
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1
Review of data.table
FreeThis chapter provides an overview of all the cool things that make data.table perfect for working with time series data, including its multiple column-selection options, how to modify data.tables by reference, and calling functions by taking advantage of non-standard evaluation.
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Generating lags, differences, and windowed aggregations
Like most other data, time series data you find in the wild are rarely suitable to directly start using in model training. In this chapter, you'll learn how to write powerful, expressive data.table code to implement a few common forms of time series feature engineering.
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Getting time series data into data.table
Ok, so you have some time series data and you believe me that data.table is great for it. Before you can test that, you need to convert your data! In this chapter, you'll learn how to convert from popular time series data formats into data.table.
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Case study: financial data
It's time to put it all together! In this chapter you'll consider a real-world dataset of spot metal prices from the London Metal Exchange (LME). By the end, you'll know how to write reusable functions to perform common time series feature engineering tasks and you'll have experience using those functions to build a statistical model.
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1
Review of data.table
FreeThis chapter provides an overview of all the cool things that make data.table perfect for working with time series data, including its multiple column-selection options, how to modify data.tables by reference, and calling functions by taking advantage of non-standard evaluation.
-
Getting time series data into data.table
Ok, so you have some time series data and you believe me that data.table is great for it. Before you can test that, you need to convert your data! In this chapter, you'll learn how to convert from popular time series data formats into data.table.
-
Generating lags, differences, and windowed aggregations
Like most other data, time series data you find in the wild are rarely suitable to directly start using in model training. In this chapter, you'll learn how to write powerful, expressive data.table code to implement a few common forms of time series feature engineering.
-
Case study: financial data
It's time to put it all together! In this chapter you'll consider a real-world dataset of spot metal prices from the London Metal Exchange (LME). By the end, you'll know how to write reusable functions to perform common time series feature engineering tasks and you'll have experience using those functions to build a statistical model.
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Lloyd's Banking Group
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Harvard Business School
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Decision Science Analytics @ USAA
James Lamb
Staff Data Scientist, Uptake
James is a Staff Data Scientist at Uptake, an Industrial IoT company based in Chicago, IL. He focuses on merging the disciplines of software engineering and data science. He is the maintainer and main author of the "uptasticsearch" R package, a co-author on the "pkgnet" package, and a member of the LightGBM development team. James holds masters degrees in Applied Economics (Marquette University) and Data Science (University of California, Berkeley).
See MoreCollaborators
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Chester Ismay
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Amy Peterson