Some Historic Forecasts

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Presentation transcript:

Some Historic Forecasts “Who the hell wants to hear actors talk?” Harry Warner, Warner Bros. Pictures, 1927 “Heavier-than-air flying machines are impossible” Lord Kelvin, President of the Royal Society, 1895 "The atom bomb will never go off - and I speak as an expert in explosives." U.S. Admiral William Leahy in 1945 "With over 50 foreign cars already on sale here, the Japanese auto industry isn't likely to carve out a big slice of the US market." Business Week, August 2, 1968

Some Historic Forecasts "Where a calculator on the ENIAC is equipped with 19,000 vacuum tubes and weighs 30 tons, computers in the future may have only 1,000 vacuum tubes and perhaps only weigh 1.5 tons." Popular Mechanics, forecasting the relentless march of science, 1949 “I think there's a world market for about five computers." Thomas J. Watson, chairman of the board of IBM. "There is no reason anyone would want a computer in their home." Ken Olson, president of Digital Equipment Corp. 1977 "While theoretically and technically television may be feasible, commercially and financially it is an impossibility." American Radio pioneer Lee DeForest, 1926 Forecasts are always wrong!

Forecasting Methods Judgement Methods: Salesforce composite: a compilation of estimates by salespeople (or dealers) of expected sales in their territories, with necessary adjustments. Scenario methods: narratives that describe an assumed future expressed as a sequence of snapshots. Delphi technique: a successive series of estimates independently developed by a group of “experts” each member of which, at each step in the process, uses a summary of the group’s previous results to formulate new estimates.

Forecasting Methods Time-series (extrapolation) methods: Moving average: averaging recent values to predict future outcomes. Exponential smoothing: forecasting the next period by calculating a weighted average of the recent data. Time-series decomposition: isolating trend, seasonal and cyclical components from a data series, and using outcome from each to make predictions.

Forecasting Methods Causal methods: Regression models: driving an equation that minimizes the variance between predictions and actual outcomes given a set of predictor (independent) variables. Econometric models: forecasts from an integrated system of simultaneous equations that represent relationships among elements of the economy.

Components of Time Series Data Stationary – mean and variance of data remain constant over time

Components of Time Series Data Trend – observations increase or decrease regularly through time Trend is the long-term component that represents the growth or decline in the time-series over an extended period of time

Components of Time Series Data Yt t Cycle – the wavelike fluctuations around the trend

Components of Time Series Data Yt t Seasonal – pattern of change that repeats itself time after time

Simple Averages

Simple Averages

Simple Averages

Simple Averages

Simple Averages

Simple Averages

Simple Averages

Moving Averages

Moving Averages

Moving Averages

Moving Averages

Moving Averages

Moving Averages

Moving Averages

Moving Averages

Moving Averages

Double Moving Averages M(t) – M’(t)

Double Moving Averages

Simple Exponential Smoothing

Simple Exponential Smoothing

Simple Exponential Smoothing

Simple Exponential Smoothing