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Time Series for Data Science

Practical Time Series Analysis for Data Science is an accessible guide that doesn't require a background in calculus to be engaging;

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Time Series Modeling of Neuroscience Data

time series An innovation-based method for spatial time series modeling for fMRI data analysis The main point of interest in this book is to;

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Practical Time Series Analysis

. What You Will Learn * Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science;

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Time Series with Python

Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks?;

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Time Series with Python

Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks?;

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An Introduction to Discrete-Valued Time Series

-Valued Time Series is a valuable working resource for researchers and practitioners in a broad range of fields, including statistics, data;

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Quantitative Economics with R: A Data Science Approach

of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and;

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Data Science for Economics and Finance

, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it;

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Data Science for Economics and Finance

, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it;

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Data Science in Theory and Practice - Techniques for Big Data Analytics and Complex Data Sets

and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various;

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Time Series Databases - New Ways to Store and Acces Data

effective ways to collect, persist, and access large-scale time series data for analysis. You'll explore the theory behind time series databases and;

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Time Series Data Analysis Using EViews

or econometric models for time series data. The procedures introduced are easily extendible to cross-section data sets. The author;

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Time Series Analysis in Climatology and Related Sciences

This book gives the reader the basic knowledge of the theory of random processes necessary for applying to study climatic time series. It;

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Practical Time Series Analysis Prediction with Statistics and Machine Learning

data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine;

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Time Series Analysis for the Social Sciences

of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time Series Analysis for the;

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Time Series Clustering and Classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts;

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Time Series Clustering and Classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts;

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Data Science for Wind Energy

maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal;

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Data Science for Wind Energy

maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal;

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Statistics for Environmental Science and Management

assessment, time series analysis, and spatial data.;

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Using Time Series to Analyze Long-Range Fractal Patterns

Using Time Series to Analyze Long Range Fractal Patterns presents methods for describing and analyzing dependency and irregularity in long;

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Data Mining In Time Series Databases

mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods;

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Exploratory Causal Analysis with Time Series Data

and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal;

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Inference for Heavy-Tailed Data

in tail index and high quantile inference. These results for independent data, dependent data, linear time series and nonlinear time series are;

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Statistical Learning for Big Dependent Data

large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data;

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Theory and Applications of Time Series Analysis

analysis. The remaining four parts include practical contributions on time series analysis in energy; complex/big data time series and forecasting;

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Python for Data science Series 1

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