There are a few good books on it actually. You should look it up.
You need the data to be homoscedastic, the errors to be independent. The independent variable(s) should lie within (or very close to) the range of observed values.
Dan Henderson vs. Rashad Evans Prediction
interval
They are interval.
A prediction is the strong belief that something will happen. It is said to be a true prediction if the event happens. Before the event takes place, you would have to ask the prophet/psychic making the prediction what it means. After the event, or after the prediction is proven false, you can see for yourself what it means.
There are many ways one might use Exponential Smoothing. Basically, Exponential Smoothing is a simple calculation one uses to collect data that allows one to predict future events.
When implemented digitally, exponential smoothing is easier to implement and more efficient to compute, as it does not require maintaining a history of previous input data values. Furthermore, there are no sudden effects in the output as occurs with a moving average when an outlying data point passes out of the interval over which you are averaging. With exponential smoothing, the effect of the unusual data fades uniformly. (It still has a big impact when it first appears.)
That, my friend, is not a question.
1) forecasting for stationary series A- Moving average B- Exponential Smoothing 2) For Trends A- Regression B- Double Exponential Smoothing 3) for Seasonal Series A- Seasonal factor B- Seasonal Decomposition C- Winters's methode
Confidence interval considers the entire data series to fix the band width with mean and standard deviation considers the present data where as prediction interval is for independent value and for future values.
Exponential Smoothing Model
G. Kallianpur has written: 'White noise theory of prediction, filtering, and smoothing' -- subject(s): Gaussian processes, Kalman filtering, Prediction theory
what exponential function is the average rate of change for the interval from x = 7 to x = 8.
Joseph V. Reilly has written: 'A dynamic inventory model using exponential smoothing'
It is called exponential because Growth of a microbial population in which cell numbers double within a specific time interval.
The linear function changes by an amount which is directly proportional to the size of the interval. The exponential changes by an amount which is proportional to the area underneath the curve. In the latter case, the change is approximately equal to the size of the interval multiplied by the average value of the function over the interval.
Time dilation, which can be derived from the Lorentz transformations is t'=t/sqrt(1-v^2/c^2) where t is the time interval in the rest frame, and t' is the interval in the lab frame. This relationship is neither linear or exponential in v.