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Practical time series analysis 1st edition by Avishek Pal, PKS Prakash ISBN 178829419X 9781788294195

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ISBN 10: 178829419X
ISBN 13: 9781788294195
Author: Avishek Pal, PKS Prakash

Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python.

The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.

The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python.

Practical time series analysis 1st Table of contents:

  1. Introduction to Time Series
  2. Different types of data
  3. Cross-sectional data
  4. Time series data
  5. Panel data
  6. Internal structures of time series
  7. General trend
  8. Seasonality
  9. Run sequence plot
  10. Seasonal sub series plot
  11. Multiple box plots
  12. Cyclical changes
  13. Unexpected variations
  14. Models for time series analysis
  15. Zero mean models
  16. Random walk
  17. Trend models
  18. Seasonality models
  19. Autocorrelation and Partial autocorrelation
  20. Summary
  21. Understanding Time Series Data
  22. Advanced processing and visualization of time series data
  23. Resampling time series data
  24. Group wise aggregation
  25. Moving statistics
  26. Stationary processes
  27. Differencing
  28. First-order differencing
  29. Second-order differencing
  30. Seasonal differencing
  31. Augmented Dickey-Fuller test
  32. Time series decomposition
  33. Moving averages
  34. Moving averages and their smoothing effect
  35. Seasonal adjustment using moving average
  36. Weighted moving average
  37. Time series decomposition using moving averages
  38. Time series decomposition using statsmodels.tsa
  39. Summary
  40. Exponential Smoothing based Methods
  41. Introduction to time series smoothing
  42. First order exponential smoothing
  43. Second order exponential smoothing
  44. Modeling higher-order exponential smoothing
  45. Summary
  46. Auto-Regressive Models
  47. Auto-regressive models
  48. Moving average models
  49. Building datasets with ARMA
  50. ARIMA
  51. Confidence interval
  52. Summary
  53. Deep Learning for Time Series Forecasting
  54. Multi-layer perceptrons
  55. Training MLPs
  56. MLPs for time series forecasting
  57. Recurrent neural networks
  58. Bi-directional recurrent neural networks
  59. Deep recurrent neural networks
  60. Training recurrent neural networks
  61. Solving the long-range dependency problem
  62. Long Short Term Memory
  63. Gated Recurrent Units
  64. Which one to use – LSTM or GRU?
  65. Recurrent neural networks for time series forecasting
  66. Convolutional neural networks
  67. 2D convolutions
  68. 1D convolution
  69. 1D convolution for time series forecasting
  70. Summary
  71. Getting Started with Python
  72. Installation
  73. Python installers
  74. Running the examples
  75. Basic data types
  76. List, tuple, and set
  77. Strings
  78. Maps
  79. Keywords and functions
  80. Iterators, iterables, and generators
  81. Iterators
  82. Iterables
  83. Generators
  84. Classes and objects
  85. Summary
 

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