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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

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 variable A {5 1 5 1 5 2 5 1} based on your knowledge of variable 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. Please visit patreon.com/fastPredict for more.

Please provide a name for the predicted variable, e.g., snow_levels. If you don't want to name, please type V1

Unless you specify variables in the file are named as V1, V2, V3, V4, V5 ...

If you would like to name your variables, please click below.

If you want to enter variable names, click here. (Computers only)

What are the variable names in the data?

Please use single words to name variables. Example: 'Impact_of_efforts' instead of 'Impact of efforts'.

If you have one variable, you should use the same variable name you have entered above.

Please upload your data file in a comma-separated file format

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

First column is for the variable which is predicted by default, last column is the last variable that can explain the predicted variable. All data should be numeric, hence please exclude the time column and columns with characters.

Observations (.csv)

Browse...

File path:

Check here if you have any knowledge for the predicted period, such as values of decision variables, or educated guesses for other variables. Otherwise, values of all variables (other than the predicted variable) for the predicted period, are also predicted first.

Decision/ guessed variables and values for the predicted period

If your variables have names (computer-use only), it can be:

Number_of_machines 2; Load_in_machines 25

Please note that variable names are alphanumeric, please use single words for variable names

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. 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 part of observations to analyze here

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

Prediction model details:

Model fit

Model parameters

Observations vs. model results

Data:

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 past 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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