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Figure:



Smaller values suggest lower impact of early conditions on new predictions




The prediction and the underlying analysis requires you to know the number of variables and number of observations for each variable. If you are predicting the 9th number in A {5 1 5 1 5 2 5 1} based on your knowledge of B {3 4 3 4 3 2 4 2}, then you have 2 variables and 8 observations.

The prediction and the underlying analysis requires you to know the number of variables and number of observations for each variable. If you are predicting the 9th number in A {5 1 5 1 5 2 5 1} based on your knowledge of B {3 4 3 4 3 2 4 2}, then you have 2 variables and 8 observations.


Please enter the variable names in your data

First variable is the predicted variable by default. Other variables are used to explain the information in the predicted variable. Please use single words to name variables. Example: 'Regional-sales' instead of 'Regional sales'.


Please key in observations with a short pause after entering each value. If your Internet connection speed is not reliable, values can take more time to stabilize.

First row is for the earliest observation, last row is the latest.
Please use single words to name variables. Example: 'Regional-sales' instead of 'Regional sales'. If you have more than one variable, first variable is the predicted variable, where others are used to improve the prediction. Because digits are not handled well, please enter integers (you can multiply by 10, 100, 1000)

Decision variables and values for current period

Please enter variable names and values

The earliest observation comes right after the variable name, please write 'NA' if there is a missing observation for a period.
Please use single words to name variables. Example: 'Regional_sales' instead of 'Regional sales'. If you have more than one variable, first variable is the predicted variable, where others are used to improve the prediction.
Sales 2 5 11 15 22; Weather 20 22 11 25 18


All variables and values for past periods


Decision variables and values for current period



By using this service you agree and accept that fastPredict.com is not responsible for the output values or interpretations of the output, any use purposes of the user, leakage of sensitive data beyond our best intentions to protect privacy. fastPredict.com does not collect or store the entered data. We store variable names and survey responses in aggregate and only for scientific research purposes and improving the service quality. fastPredict.com respects privacy of user data and has no interest in cookies. Instead we appreciate well-being of fastPredict.com users. The provided service aims to enhance the analytic capabilities of users, but it is free of any guarantees. All responsibility belongs to the user.










Prediction model details:

Model fit

Model parameters

Observations vs. model results


Prediction boundaries are limit levels within the 95% prediction interval
We use 'TS' in short to say 'Time series'. These models require each observation to have the same frequency, e.g., weekly, monthly, quarterly, yearly observations
Conditional means do not require a temporal sequence
For variables that have constant growth rates, time trend is a necessary variable to account for that type of growth characteristic (#1, #2, #3, #4 and #5)
Seasonality effects is a common term used to account for periodic patterns that influence a variable, e.g., temperature and months (#2 #3 ,#4 and #5)
Dynamic effects arise when the current value of a variable depends on previously observed values of the same variable. (#3 and #5 in single variable and multivariable models)
Long term memory effects are observed when starting conditions have intertemporal effects (#4 and #5).



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