Basic Methods of Time Series Analysis
Jürgen W. Einax, Heinz W. Zwanziger, Sabine Geiß · 1997
This chapter contains sections titled: Introduction Example: Nitrate Loadings in a Drinking Water Reservoir – Description of the Problem Plotting Methods Time Series Plot Seasonal Sub-Series Plot Smoothing and Filtering Simple Moving Average Exponential Smoothing Simple and Seasonal Differencing and the CUSUM Technique Seasonal Decomposition Regression Techniques Trend Evaluation with Ordinary Least Squares Regression Least Squares Regression with an Explanatory Variable Least Squares Regression with Dummy Variables (Multiple Least Squares Regression) Correlation Techniques Autocorrelation, Autoregression, Partial Autocorrelation, and Cross-correlation Function Autoregression Analysis – Regression with an Explanatory Variable Multivariate Auto- and Cross-correlation Analysis ARIMA Modeling Mathematical Fundamentals Application of ARIMA Models Specification of ARIMA Models Application of the ARIMA Modeling to the Example Time Series Forecasting with ARIMA