Forecasting with exponential smoothing Anne B. Koehler, J. Keith Ord, Ralph D. Snyder, Rob Hyndman
Publisher: Springer

Fill in the table by preparing forecasts based on a five- year moving average, a three- year moving average, and exponential smoothing( with a w = 0.9 and a w = 0.3). Importance of data diminishes as the past becomes more distant. However, forecasts today give greater emphasis on the more recent demand data than the older data. Develop a forecast for years 2 through 12 using exponential smoothing with = .4 and a forecast for year 1 of 6. Forecast - Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. Linear Smoothing; similar to method 9, a formula is assigned to give weights. Quantitative forecasting techniques require varying amounts of historical information. For univariate forecasting, you can use Exponential smoothing models, intervention models, Box-Jenkins models, distributed lag models, vector autoregression models etc. In recent years, with the rapid development of science and technology, economy and society have made great progress, meanwhile a large mount of date such as agricultural prices have been produced in various fields. This is called smoothing and is produced by giving more weight to the recent data. Each increment in the past is decreased. Simple Exponential Smoothing - 52 wins (This type of forecasting places greater emphasis on more recent data points. I had never really pursued time series forecasting in any structured way before this. For short-range forecasts of mature products; Exponential Smoothing; this method is useful to forecast demand when no linear trend is in the data. Off-the-shelf products like Forecast Pro or Autobox. However today while searching I came across this website and got pretty excited . The most used of all forecasting techniques. Statistical forecasting enables you to use detailed history and applies weighting factors to exponentially smooth the data. Plot your new forecast on a graph with the actual data and the naive forecast.