Data Mining for Business Analytics

CHAPTER 16Handling Time Series

In this chapter, we describe the context of business time series forecasting and introduce the main approaches that are detailed in the next chapters, and in particular, regression-based forecasting and smoothing-based methods. Our focus is on forecasting future values of a single time series. These three chapters are meant as an introduction to the general forecasting approach and methods.

In this chapter, we discuss the difference between the predictive nature of time series forecasting vs. the descriptive or explanatory task of time series analysis. A general discussion of combining forecasting methods or results for added precision follows. Next, we present a time series in terms of four components (level, trend, seasonality, and noise) and present methods for visualizing the different components and for exploring time series data. We close with a discussion of data partitioning (creating training and validation sets), which is performed differently from cross-sectional data partitioning.

Python

In this chapter, we will use pandas for data handling and matplotlib for visualization. Models are built using statsmodels.

 import required functionality for this chapter

import pandas as pd
import matplotlib.pylab as plt
import statsmodels.formula.api as sm
from statsmodels.tsa import tsatools
from dmba import regressionSummary

16.1 Introduction ...