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Figure:

Coefficient for the smoothing parameter for relevant models that partially controls initial condition effects

Smaller values suggest lower impact of early conditions on new predictions

Preference for use-interface

Sensitive interface appears nice to enter data, with a spreadsheet look, but as the name suggests, a bit sensitive to the Internet speed etc. For a reliable experience we recommend, the Robust Interface. fP

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.

How many variables do you have?

How many observations do you have?

fastPredict.com offers the ability to analyze upto 2 variables and 20 observations per variable. Boost to 5 variables and 50 observations by typing fastPredict here.

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.

How many variables do you have?

How many observations do you have?

fastPredict.com offers the ability to analyze up to 2 variables and 20 observations per variable. Boost to 5 variables and 50 observations by typing fastPredict here.

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: 'Impact_of_effort' instead of 'Impact of effort'.

The first variable is the predicted variable, other variables are used to improve the prediction.

First row is for the earliest observation, last row is the latest.

Please key in observations with a short pause after entering each value. Values can take some time to stabilize.

Digits are not handled well, please enter integers (you can multiply by 10, 100, 1000)

Check here if you have any knowledge of explanatory variables, i.e., any of the variables other than the predicted variable.

Decision variables and values for current period

Please enter only one set of observations, that is, one row.

Variables and Data

The first variable is the predicted variable, other variables help improve the prediction.

The earliest/ oldest observation comes immediately after the variable name.

Use single words to label variables. Example: 'Impact_of_fun' instead of 'Impact of fun'.

Impact 2 5 11 15 22; Effort 20 22 11 25 18; Support NA 5 2 7 8

Please write 'NA' if there is a missing observation for a period.

That's all. Have a fastPredict!

Check here if you have any knowledge of explanatory variables, i.e., any of the variables other than the predicted variable.

Explanatory variables for current period

Please enter only one set of observations, that is, one row.

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 data beyond our best intentions for privacy. fastPredict.com does not collect or store data other than variable names and survey responses in aggregate and only for scientific purposes and/ or improving service quality. fastPredict.com respects privacy of user data and has no interest in cookies either. Instead we appreciate the 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.

You can update the analyzed variable, i.e., dependent variable, here

Prediction model details:

Model fit

Model parameters

Observations vs. model results

Prediction boundaries are limit levels within the 95% prediction interval

All models require each observation to have the same frequency, e.g., weekly, monthly, quarterly, yearly observations

A type models only uses information about the predicted variable. B type models include information from other variables.

Seasonality effects is a common term used to account for periodic patterns that influence a variable, e.g., temperature and months (A2, A3 ,A4, A5, B2, B3, B4, B5)

Dynamic effects arise when the current value of a variable depends on previously observed values of the same variable. (A3, A5, B3 and B5)

Long term memory effects are observed when starting conditions have dynamic effects (A4, A5, B4 and B5).

Thank you for predicting with us!

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