is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. A time series is a set of numerical data collected over time. Due to differences in the features of data for various investments described in the Using Statistics scenario, you need to consider several different approaches for forecasting time-series data.
This lecture begins with an introduction to the importance of business forecasting and a description of the components of time-series models. The coverage of forecasting models begins with annual time-series data. First section presents moving averages and exponential smoothing methods for smoothing a series. This is followed by least-squares trend fitting and forecasting in the next section and autoregressive modeling in the following section. After lecture discusses how to choose among alternative forecasting models. Finally lecture develops models for monthly and quarterly time series.
- To construct different time-series forecasting models: moving averages, exponential smoothing, linear trend, quadratic trend, exponential trend, autoregressive models, and least squares models for seasonal data
- To choose the most appropriate time-series forecasting model
In this lecture we discussed:
- The importance of forecasting
- The component factors of the time-series model
- The smoothing of data series
- Moving averages
- Exponential smoothing
- Least square trend fitting and forecasting
- Linear, quadratic and exponential models
- Autoregressive models
- A procedure for choosing appropriate models
- Time-series forecasting of monthly or quarterly data by using dummy variables
- Pitfalls concerning time-series analysis
In this File you will find:
Time-Series Forecasting Lecture Power Point Presentation
Test Bank for Time-Series Forecasting with 170 Questions with all answers to them
64 Exercises for Time-Series Forecasting seminar or lecture
Plus reading resource on Time-Series Forecasting in order to enhance you overall knowledge about the topic.
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