1. The goal of this chapter is to give you an overview of time series
analysis and forecasting. As we all know, every business or company
was supposed to provide quarterly sales forecasts for one of their
company's products over the next one-year period. The quarterly
forecasts we provide will have an impact on production schedules, raw
material purchasing, inventory policies, and sales quotas. As a result,
poor forecasting may lead to poor planning and increased costs for the
company.
2. What is Forecast is simply a prediction of what will happen in the
future.
Forecasting also aids commercial decision-making. It also helps
managers predict business indicators like sales expectations and
consumer behavior, regardless of firm size. Forecasting is essential but
requires expertise and accurate data.
How should we present the quarterly sales forecasts? Good judgment,
intuition, and an understanding of the state of the economy can
provide us with a rough idea or "feeling" of what is likely to happen in
the future, but converting that feeling into a number that can be used
as a sales forecast for next year is difficult.
3. So, in this discussion we will focus exclusively on quantitative
forecasting methods in this chapter.
4. The objective of time series analysis is to uncover a pattern in the
historical data or time series and then extrapolate the pattern into the
future; the forecast is based solely on past values of the variable
and/or on past forecast errors.
5. The measurements may be taken every hour, day, week, month, or
year, or at any other regular interval. The pattern of the data is an
important factor in understanding how the time series has behaved in
the past. If such behavior can be expected to continue in the future,
we can use it to guide us in selecting an appropriate forecasting
method.
To identify the underlying pattern in the data, a useful first step is to
construct a time series plot.
6. To illustrate a time series with a horizontal pattern, consider the 12
weeks of data
These data show the number of gallons of gasoline (in 1000s) sold by a
gasoline distributor in Bennington, Vermont, over the past 12 weeks. The
average value or mean for this time series is 19.25 or 19.250 gallons per
week. It also shows a time series plot for these data. Note how the data
fluctuate around the sample mean of 19.250 gallons. Although random
variability is present, we would say that these data follow a horizontal
pattern. To get 19.250 all you need to do is add all the sales of gallons
and divide it into number of weeks.
7. A time series plot for a stationary time series will always exhibit a
analysis and forecasting. As we all know, every business or company
was supposed to provide quarterly sales forecasts for one of their
company's products over the next one-year period. The quarterly
forecasts we provide will have an impact on production schedules, raw
material purchasing, inventory policies, and sales quotas. As a result,
poor forecasting may lead to poor planning and increased costs for the
company.
2. What is Forecast is simply a prediction of what will happen in the
future.
Forecasting also aids commercial decision-making. It also helps
managers predict business indicators like sales expectations and
consumer behavior, regardless of firm size. Forecasting is essential but
requires expertise and accurate data.
How should we present the quarterly sales forecasts? Good judgment,
intuition, and an understanding of the state of the economy can
provide us with a rough idea or "feeling" of what is likely to happen in
the future, but converting that feeling into a number that can be used
as a sales forecast for next year is difficult.
3. So, in this discussion we will focus exclusively on quantitative
forecasting methods in this chapter.
4. The objective of time series analysis is to uncover a pattern in the
historical data or time series and then extrapolate the pattern into the
future; the forecast is based solely on past values of the variable
and/or on past forecast errors.
5. The measurements may be taken every hour, day, week, month, or
year, or at any other regular interval. The pattern of the data is an
important factor in understanding how the time series has behaved in
the past. If such behavior can be expected to continue in the future,
we can use it to guide us in selecting an appropriate forecasting
method.
To identify the underlying pattern in the data, a useful first step is to
construct a time series plot.
6. To illustrate a time series with a horizontal pattern, consider the 12
weeks of data
These data show the number of gallons of gasoline (in 1000s) sold by a
gasoline distributor in Bennington, Vermont, over the past 12 weeks. The
average value or mean for this time series is 19.25 or 19.250 gallons per
week. It also shows a time series plot for these data. Note how the data
fluctuate around the sample mean of 19.250 gallons. Although random
variability is present, we would say that these data follow a horizontal
pattern. To get 19.250 all you need to do is add all the sales of gallons
and divide it into number of weeks.
7. A time series plot for a stationary time series will always exhibit a
