#24-time-series-introduction-solution.doc - Stat252 winter2019 Handout#24 Introduction to Time Series Chapter 19(19.1-19.2 Week 10(Tuesday – Friday

#24-time-series-introduction-solution.doc - Stat252...

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Stat252 winter2019 Handout#24 Introduction to Time Series: Chapter 19(19.1-19.2) 1 Week 10 (Tuesday – Friday) : Short Introduction to Time Series Time Series … what makes it different? (Handout #24 – today’s handout) Data collected over time Usually intervals are equally spaced Time Series Component (or Patterns) Two Main Goals: (a) identifying the nature of the phenomenon represented by the sequence of observations(identify the patterns) (b) forecasting (predicting future values of the time series variable). Both of these goals require that the pattern of observed time series data is identified and more or less formally described . Once the pattern is established, we can interpret and analyze that data. Regardless of the depth of our understanding and the validity of our interpretation (theory) of the phenomenon, we can extrapolate the identified pattern to predict future events . Selecting a Forecasting Method (or Selecting a Time Series Model) Moving Averages (Handout#26- THURSDAY) Regression Based Models (Handout #25- Wednesday) Accuracy Measures (Lab#8 and Handout#25-#26 Lab#8) Forecast Error = Actual Value – Forecast Forecast Accuracy Measures: MAD, MSE, and MAPE to compare Forecasting Methods (or Time series Models) Today: Introduction to Time series data and Time Series Patterns A time series is a collection of observations of well-defined data items obtained through repeated measurements over time. As in most other analyses, in time series analysis it is assumed that the data consist of a systematic pattern (usually a set of identifiable components) and random noise (error) which usually makes the pattern difficult to identify.
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