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High-level API for creating forecasting dashboards.

Project description

Dash-Forecast allows you to easily build forecasting dashboards.

Why Dash-Forecast

Judgment and decision making research shows that visual tools are an easy and effective way to boost forecasting accuracy. Dash-Forecast is a high-level API for creating beautiful forecasting visualizations and statistical summaries.


$ pip install dash-fcast


In just a few lines of code, we'll create an app that gives you:

  1. An intuitive 'bounds and moments' forecast elicitation
  2. An editable data table representation of the forecast
  3. Probability density function and cumulative distribution function line plots of the forecast
  4. A bar plot of the data table

Create a file

import dash_fcast as fcast
import dash_fcast.distributions as dist

import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go
from dash.dependencies import Input, Output

app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])

app.layout = html.Div([
        datatable={'editable': True, 'row_deletable': True},
], className='container')


    Output('graphs', 'children'),
        Input(dist.Moments.get_id('Forecast'), 'children'),
        Input(fcast.Table.get_id('Table'), 'children')
def update_graphs(dist_state, table_state):
    distribution = dist.Moments.load(dist_state)
    table = fcast.Table.load(table_state)
    pdf = go.Figure([distribution.pdf_plot(), table.bar_plot('Forecast')])
    pdf.update_layout(transition_duration=500, title='PDF')
    cdf = go.Figure([distribution.cdf_plot()])
    cdf.update_layout(transition_duration=500, title='CDF')
    return [dcc.Graph(figure=pdf), dcc.Graph(figure=cdf)]

if __name__ == '__main__':

Run your application with:

$ python

Open your browser and navigate to http://localhost:8050/.


  author = {Dillon Bowen},
  title = {Dash-Forecast},
  url = {},
  date = {2020-09-11},


Users must cite this package in any publications which use it.

It is licensed with the MIT License.


The following collaborators deserve special acknowledgement:

  • David Melgin, for the bounds and moments elicitation
  • Ezra Karger, whose non-parametric elicitation methods helped inspire my 'tabular elicitation'
  • Sarah Reed, for feedback on the front-end design

I would also like to thank the Tetlock Lab, whose weekly presentations inspired various aspects of this package, including Zachary Jacobs' and Ian Lustick's 'first approximation algorithm', Scott Page's multi-model thinking, and Annie Duke's presentation on intuitively eliciting predictions.

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