#!/usr/bin/env python
# coding: utf-8

# # Video: Time Series Forecasting in Power BI
# > Recording of my presentation at Global AI Bootcamp, Singapore on Jan 17, 2021 
# 
# - toc: true 
# - badges: true
# - comments: true
# - categories: [PowerBI, forecasting, ETS]
# - hide: false

# ## Forecasting in Power BI
# 
# I have written and talked about Time Series Forecasting previously, especially on how to create forecasts in Power BI. You can refer to my previous blog posts below:
# 
# **Time series Forecasting in Python & R, Part 1 (EDA)**: [Link](https://pawarbi.github.io/blog/forecasting/r/python/rpy2/altair/2020/04/21/timeseries-part1.html) This blog covers exploratory data analysis techniques for time series data before creating the forecast. 
# 
# **Time series Forecasting in Python & R, Part 2 (Forecasting)**: [Link](https://pawarbi.github.io/blog/forecasting/r/python/rpy2/altair/fbprophet/ensemble_forecast/uncertainty/simulation/2020/04/21/timeseries-part2.html) Here I go into theoretical and practical aspects of creatings forecasts using many different classical forecasting methods
# 
# **Time series Forecasting in Power BI**: [Link](https://pawarbi.github.io/blog/forecasting/python/powerbi/forecasting_in_powerbi/2020/04/24/timeseries-powerbi.html) This blog shows how to create forecasts in Power BI, its strengths, limitations, and my recommendations.   

# I recently presented on this topic at [Global AI Bootcamp, Singapore](http://www.aibootcampsg.com/). Here is the recording of that presentation. It summarizes my findings from the blog above.
# 
# 

# >youtube: https://t.co/TgzJsMUncd?amp=1

# In[ ]: