How to deploy Time Series Forecasting models Using StreamLit

time series forecasting using streamlit is an open-source project which provides an interactive framework for Data Science experimentations. We have already covered real-time object detection and building a COVID-19 dashboard using the Streamlit API in our previous articles. In this article, we are going to use Streamlit for another use case called time series forecasting.

Before proceeding, refer to this post if you are unaware of the Streamlit framework. 

Here, we have used the Anaconda prompt and Python version 3.8.5 for the implementation. Facebook’s Prophet open-source library has been used for forecasting (Python API for Prophet). The code allows the user to upload custom time-series data and visualise the Prophet’s forecast in Streamlit app on a web browser. Stepwise explanation of the code is as follows:


Sign up for your weekly dose of what's up in emerging technology.
  1. Import required libraries and classes.
 #Import streamlit
 import streamlit as st
 #Import NumPy and Pandas for data manipulation 
 import pandas as pd
 import numpy as np
 from fbprophet import Prophet
 from fbprophet.diagnostics import performance_metrics
 from fbprophet.diagnostics import cross_validation
 from fbprophet.plot import plot_cross_validation_metric
#for encoding binary data to printable ASCII characters and decoding it  #back to binary form
 import base64  
  1. Define the title to be displayed on the top of UI using streamlit.title()

st.title('Time Series Forecasting Using Streamlit')

  1. Text to be displayed below the title regarding instructions to import the data. (Refer to this page to know the kind of input data frame expected by Prophet)

streamlit.write() is used to write arguments to the app

 st.write("IMPORT DATA")
 st.write("Import the time series CSV file. It should have two columns labelled as 'ds' and 'y'. The 'ds' column should be of DateTime format by Pandas. The 'y' column must be numeric representing the measurement to be forecasted.") 

The title and the text will appear in the UI as follows:

  1. Insert a file uploader widget using streamlit.file_uploader() so that the user can browse and upload .csv data file from his local machine. Store the uploaded data into a data frame called ‘data.’

data = st.file_uploader('Upload here',type='csv')

The file uploader widget will look something like this:

  1. The .csv file that we have uploaded for the demo can be found here
 if data is not None:
     appdata = pd.read_csv(data)  #read the data fro
     appdata['ds'] = pd.to_datetime(appdata['ds'],errors='coerce') 
     st.write(data) #display the data  
      max_date = appdata['ds'].max() #compute latest date in the data 

Some initial records of the uploaded file will appear below the widget as follows:

  1.  Choose the forecast horizon

st.write("SELECT FORECAST PERIOD") #text displayed

Insert a numeric input widget using streamlit.number_input() so that the user can select the number of days for which he wants to forecast the future.

 periods_input = st.number_input('How many days forecast do you want?',
 min_value = 1, max_value = 365)
#The minimum number of days a user can select is one, while the maximum is  #365 (yearly forecast) 

The section for horizon selection containing the numeric input widget will appear in UI as follows:

  1. Fit the time series data for making forecast using Facebook Prophet
 if data is not None:
     obj = Prophet() #Instantiate Prophet object  #fit the data 
  1. Visualize the forecasted data
#text to be displayed
 st.write("The following plot shows future predicted values. 'yhat' is the  
 predicted value; upper and lower limits are 80% confidence intervals by  
 if data is not None:
     future = obj.make_future_dataframe(periods=periods_input)
#Prophet.make_future_dataframe() takes the Prophet model object and   #extends the time series dataframe for specified period for which user needs #the forecast
     fcst = obj.predict(future)  #make prediction for the extended data
     forecast = fcst[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]
#The predict() method assigns each row in the ‘future’ dataframe a predicted #value denoted as yhat
#Choose only the forecasted records (having date after the latest date in #original data)
      forecast_filtered =  forecast[forecast['ds'] > max_date]    
      st.write(forecast_filtered)  #Display some forecasted records
      st.write(“The next visual shows the actual (black dots) and predicted 
      (blue line) values over time.”)    
      figure1 = obj.plot(fcst) #plot the actual and predicted values
      st.write(figure1)  #display the plot
 #Plot the trends using Prophet.plot_components()
      st.write("The following plots show a high level trend of predicted     
      values, day of week trends and yearly trends (if dataset contains   
      multiple years’ data).Blue shaded area represents upper and lower  
      confidence intervals.")
      figure2 = obj.plot_components(fcst) 

How to run the code?

  1. In your terminal, install fbprophet Python library

pip install fbprophet

IMP NOTE: If you get issues in installing fbprophet using pip command in Anaconda prompt, install it using conda as follows:

 conda install gcc
 conda install -c conda-forge fbprophet 
  1. Install streamlit as follows:

pip install --upgrade streamlit

  1. Suppose you have saved the code file by the name

Then, run the following command on the terminal, open the Streamlit app in your browser and run your application.

streamlit run FILEPATH

where FILEPATH is the path to the location where you have stored the file.


When the app gets launched, the UI appears as follows:

On uploading the .csv file, suppose we select 100 as the number of days for which forecast should be made. Some of the forecasted records will appear on the UI as follows:

Plot showing actual values (black dots) and predicted values (blue):

Plots showing forecasted future trend:


Refer to the following sources to know about Streamlit:

More Great AIM Stories

Nikita Shiledarbaxi
A zealous learner aspiring to advance in the domain of AI/ML. Eager to grasp emerging techniques to get insights from data and hence explore realistic Data Science applications as well.

Our Upcoming Events

Conference, in-person (Bangalore)
Machine Learning Developers Summit (MLDS) 2023
19-20th Jan, 2023

Conference, in-person (Bangalore)
Rising 2023 | Women in Tech Conference
16-17th Mar, 2023

Conference, in-person (Bangalore)
Data Engineering Summit (DES) 2023
27-28th Apr, 2023

Conference, in-person (Bangalore)
MachineCon 2023
23rd Jun, 2023

Conference, in-person (Bangalore)
Cypher 2023
20-22nd Sep, 2023

3 Ways to Join our Community

Whatsapp group

Discover special offers, top stories, upcoming events, and more.

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Subscribe to our newsletter

Get the latest updates from AIM