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)

Advanced processing and visualization of time series data


In many cases, the original time series needs to be transformed into aggregate statistics. For example, observations in the original time series might have been recorded at every second; however, in order to perform any meaningful analysis, data must be aggregated every minute. This would need resampling the observations over periods that are longer than the granular time indices in the original data. The aggregate statistics, such as mean, median, and variance, is calculated for each of the longer periods of time.

Another example of data pre-processing for time series, is computing aggregates over similar segments in the data. Consider the monthly sales of cars manufactured by company X where the data exhibits monthly seasonality, due to which sales during a month of a given year shows patters similar to the sales of the same month in the previous and next years. To highlight this kind of seasonality we must remove the long-run trend...