The book contains the most common as well as state-of-the-art methods in machine learning for time-series, and examples that every data scientist or analyst would have encountered, if not in their job, then in a job interview.
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"Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook’s open-source Prophet model, …
Cover all the machine learning techniques relevant for forecasting problems, ranging from univariate and multivariate time series to supervised learning, to state-of-the-art deep forecasting models such as LSTMs, recurrent neural networks, Facebook's open-source Prophet model, a…
Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis. You'll explore the theory behind time series databases…
Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis. You'll explore the theory behind time series databases…
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Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis. You'll explore the theory behind time series databases…
Annotation
"Provides a step-by-step guide on how to use various publicly-available, remotely-sensed time series data sources for environmental monitoring and assessment. Readers will learn how extract valuable information on global changes from a 20-year collection of ready-to-use remotely…
"Provides a step-by-step guide on how to use various publicly-available, remotely-sensed time series data sources for environmental monitoring and assessment. Readers will learn how extract valuable information on global changes from a 20-year collection of ready-to-use remotely…
This book explains the concepts of time series from traditional to bleeding-edge techniques with full-fledged examples. The book begins by covering time series fundamentals and its characteristics, the structure of time series data, pre-processing, and ways of crafting the featu…
This book explains the concepts of time series from traditional to bleeding-edge techniques with full-fledged examples. The book begins by covering time series fundamentals and its characteristics, the structure of time series data, pre-processing, and ways of crafting the featu…
"This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughou…
"This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughou…
This book emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, it presents a coherent and systematic regres…
Written at a readily accessible level, Basic Data Analysis for Time Series with R emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the con…