Practical time-series analysis : master time series data processing, visualization, and modeling using python

Practical time-series analysis master time series data processing, visualization, and modeling using python

Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis - time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory,...

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Detalles Bibliográficos
Otros Autores: Pal, Avishek, author (author), Prakash, PKS, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England ; Mumbai, [India] : Packt 2017.
Edición:1st edition
Materias:
Time-series analysis.
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631534706719
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Authors
  • About the Reviewer
  • www.PacktPub.com
  • Customer Feedback
  • Table of Contents
  • Preface
  • Chapter 1: Introduction to Time Series
  • Different types of data
  • Cross-sectional data
  • Time series data
  • Panel data
  • Internal structures of time series
  • General trend
  • Seasonality
  • Run sequence plot
  • Seasonal sub series plot
  • Multiple box plots
  • Cyclical changes
  • Unexpected variations
  • Models for time series analysis
  • Zero mean models
  • Random walk
  • Trend models
  • Seasonality models
  • Autocorrelation and Partial autocorrelation
  • Summary
  • Chapter 2: Understanding Time Series Data
  • Advanced processing and visualization of time series data
  • Resampling time series data
  • Group wise aggregation
  • Moving statistics
  • Stationary processes
  • Differencing
  • First-order differencing
  • Second-order differencing
  • Seasonal differencing
  • Augmented Dickey-Fuller test
  • Time series decomposition
  • Moving averages
  • Moving averages and their smoothing effect
  • Seasonal adjustment using moving average
  • Weighted moving average
  • Time series decomposition using moving averages
  • Time series decomposition using statsmodels.tsa
  • Summary
  • Chapter 3: Exponential Smoothing based Methods
  • Introduction to time series smoothing
  • First order exponential smoothing
  • Second order exponential smoothing
  • Modeling higher-order exponential smoothing
  • Summary
  • Chapter 4: Auto-Regressive Models
  • Auto-regressive models
  • Moving average models
  • Building datasets with ARMA
  • ARIMA
  • Confidence interval
  • Summary
  • Chapter 5: Deep Learning for Time Series Forecasting
  • Multi-layer perceptrons
  • Training MLPs
  • MLPs for time series forecasting
  • Recurrent neural networks
  • Bi-directional recurrent neural networks
  • Deep recurrent neural networks.
  • Training recurrent neural networks
  • Solving the long-range dependency problem
  • Long Short Term Memory
  • Gated Recurrent Units
  • Which one to use - LSTM or GRU?
  • Recurrent neural networks for time series forecasting
  • Convolutional neural networks
  • 2D convolutions
  • 1D convolution
  • 1D convolution for time series forecasting
  • Summary
  • Appendix: Getting Started with Python
  • Installation
  • Python installers
  • Running the examples
  • Basic data types
  • List, tuple, and set
  • Strings
  • Maps
  • Keywords and functions
  • Iterators, iterables, and generators
  • Iterators
  • Iterables
  • Generators
  • Classes and objects
  • Summary
  • Index.

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