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)

First order exponential smoothing


First order exponential smoothing or simple exponential smoothing is suitable with constant variance and no seasonality. The approach is usually recommended to make short-term forecast. Chapter 2, Understanding Time-series data, has introduced the naïve method for the forecasting where prediction in horizon h is defined as value of t (or the last observation):

xt+h = xt

The approach is extended by simple moving average, which extends the naïve approach using the mean of multiple historical points:

The approach assumes equal weight to all historical observations, as shown in the following figure:

Figure 3.4: Weight assigned to observation with increasing window size

As the window size for moving average increases, the weights assigned to each observation become smaller. The first order exponential extends this current approach by providing exponential to historical data points where weights decrease exponentially from the most recent data point to the oldest....