Book Image

Practical Time Series Analysis

By : Avishek Pal, PKS Prakash
Book Image

Practical Time Series Analysis

By: Avishek Pal, PKS Prakash

Overview of this book

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.
Table of Contents (13 chapters)

Chapter 2. Understanding Time Series Data

In the previous chapter, we touched upon a general approach of time series analysis which consists of two main steps:

  • Data visualization to check the presence of trend, seasonality, and cyclical patterns
  • Adjustment of trend and seasonality to generate stationary series

Generating stationary data is important for enhancing the time series forecasting model. Deduction of the trend, seasonal, and cyclical components would leave us with irregular fluctuations which cannot be modeled by using only the time index as an explanatory variable. Therefore, in order to further improve forecasting, the irregular fluctuations are assumed to be independent and identically distributed (iid) observations and modeled by a linear regression on variables other than the time index.

For example, house prices might exhibit both trend and seasonal (for example, quarterly) variations. However, the residuals left after adjusting trend and seasonality might actually depend on...