Abstract
In the previous chapter, you learned about using traditional techniques to work with time-series data. In this chapter, you will learn how to solve univariate time-series problems using bleeding-edge techniques. A univariate time series is a time series that consists of single (scalar) observations recorded sequentially over equally spaced time periods. In this chapter, you will look at single-step time-series forecasting and horizon-style time-series forecasting.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 B V Vishwas and Ashish Patel
About this chapter
Cite this chapter
Vishwas, B.V., Patel, A. (2020). Bleeding-Edge Techniques for Univariate Time Series. In: Hands-on Time Series Analysis with Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-5992-4_6
Download citation
DOI: https://doi.org/10.1007/978-1-4842-5992-4_6
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-5991-7
Online ISBN: 978-1-4842-5992-4
eBook Packages: Professional and Applied ComputingApress Access BooksProfessional and Applied Computing (R0)