Nicolas Kuhaupt that in fact Probabilistic Projecting along with DeepAR and AWS SageMaker

„Probabilistic Forecasting with DeepAR and AWS SageMaker
EuroPython 2020 – Talk – 2020-07-24 – Parrot Data Science
Online

By Nicolas Kuhaupt

In time series forecasting we are interested in how the time series is going to continue in the future. This is of high importance in areas like forecasting energy production from renewable resources, forecasting demand of customers or the price of products. Many forecasting algorithms provide only the prediction. However, oftentimes we are also interested in the likelihood of the prediction and how much it may vary. This is what probabilistic forecasting is for. With every forecast, we also obtain an upper and lower bound with certain probabilities. For a long time, probabilistic forecasting was limited to traditional techniques like ARIMA. DeepAR is an algorithm that allows us to combine Deep Learning techniques with probabilistic forecasting. Additionally, in contrast to training a model for each time series individually, DeepAR suggests training one large forecasting model for all related time series. The algorithm was developed by Amazon and is also provided in AWS SageMaker.
In this talk, we will understand the theoretical basics of DeepAR, have a look at a practical time series example and will demonstrate an implementation. In the end, you will be prepared to get started with your own forecasts.

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Time-Series Study along with R | 2 or more. Projecting

Provides steps for carrying out time-series analysis with R and covers forecasting stage.
Previous video – time-series decomposition: https://goo.gl/hRJmU1
Next video – time-series clustering: https://goo.gl/5gMryj
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R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
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The right time Assortment Projecting quickly

In this Data Science in Minutes, we will describe what time series forecasting is, and provide several examples of when you can use time series for your data. Time series forecasting is looking at data over time to forecast or predict what will happen in the next time period, based on patterns or re-occurring trends of previous time periods.

Check out our time series tutorial:

Time Series in Python Part 1: Read and Transform Your Data



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Introduction of Valuable time Combination of Projecting | Step 7 (seven) | ARIMA Forecasting real life Example in R

Introduction of Time Series Forecasting | Part 7 | ARIMA Forecasting real life Example in R

Hi guys.. in this part 6 of time series forecasting video series I have taken a real life example of rain fall in india and predicted the future years rains with by producing the arima model and then using the forecast package, I predicted the next few years rain fall values.

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PyData London Meetup #47
Tuesday, August 14, 2018

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Forecasting and Regression
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