Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Table of contents
1.
Introduction
2.
Built-in Forecast Algorithms
2.1.
1️⃣ARIMA
2.2.
2️⃣CNN-QR
2.3.
3️⃣DeepAR+
2.4.
4️⃣Prophet
2.5.
5️⃣NPTS
3.
6️⃣ETS
4.
Frequently Asked Questions
4.1.
How does Amazon forecasting work?
4.2.
What is a forecasting method?
4.3.
How does Amazon Forecast its demand?
4.4.
Does Amazon use Amazon Forecast?
5.
Conclusion
Last Updated: Mar 27, 2024

Amazon Forecast Algorithms

Author GAZAL ARORA
1 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

Amazon Forecast is a fully managed time-series forecasting service built for business metrics research and based on machine learning. You don't require prior knowledge of machine learning to get started. You simply need to input historical data and any extra information you think might affect your forecasts.

Amazon Forecast Algorithms

An Amazon Forecast predictor utilizes an algorithm to train a model with your time-series datasets. This trained model is used to generate metrics and predictions.

If you're unsure which method to employ to train your model, create a predictor using AutoML and let Forecast train the best model for your datasets. Otherwise, you can choose one of the Amazon Forecast algorithms manually.

Built-in Forecast Algorithms

You can choose from six built-in algorithms in Amazon Forecast. These algorithms range from commonly used statistical algorithms like ARIMA to complicated neural network algorithms like CNN-QR and DeepAR+. These are:

  1. ARIMA
     
  2. CNN-QR
     
  3. Prophet
     
  4. DeepAR+
     
  5. ETS
     
  6. NPTS

1️⃣ARIMA

Autoregressive Integrated Moving Average (ARIMA) is a widely used local statistical algorithm for time-series forecasting. ARIMA captures the input dataset's standard temporal structures (patterned time organization). 

The ARIMA algorithm is helpful for datasets that can be mapped to stationary time series. The statistical properties of stationary time series are independent of time. Datasets with stationary time series contain a combination of signal and noise. The signal could have a sinusoidal oscillation pattern or a seasonal component. ARIMA separates the signal from the noise with a filter, then extrapolates the signal into the future to create predictions.

2️⃣CNN-QR

Amazon Forecast CNN-QR, or Convolutional Neural Network - Quantile Regression, is a machine learning algorithm that uses causal convolutional neural networks to anticipate scalar (one-dimensional) time series (CNNs). This supervised learning approach employs a quantile decoder to create probabilistic predictions after training one global model from a huge collection of time series.

CNN-QR is a probabilistic forecasting sequence-to-sequence (Seq2Seq) model that evaluates how effectively a prediction reconstructs the decoding sequence based on the encoding sequence.

3️⃣DeepAR+

DeepAR+ is a supervised learning Amazon Forecast algorithm that uses recurrent neural networks to forecast scalar (one-dimensional) time series (RNNs). Traditional forecasting methods, such as ARIMA or ETS, fit a single model to each time series and use that model to extrapolate the time series into the future. On the other hand, many applications have similar time series spread throughout cross-sectional units. 

This algorithm can be beneficial for training a single model across all-time series. The DeepAR+ algorithm outperforms the regular ARIMA and ETS algorithms when your dataset has hundreds of feature time series.

4️⃣Prophet

Prophet is an additive time series forecasting algorithm that fits non-linear patterns with yearly, monthly, and daily seasonality.

Prophet is useful for datasets that:

  • Contain extensive historical observations over a long period (months or years) (hourly, daily, or weekly).
     
  • Have several distinct seasons.
     
  • Include previously known significant but irregular events.
     
  • Have any missing data points or big outliers.
     
  • Have non-linear growth patterns that are reaching a limit.

5️⃣NPTS

The Nonparametric Time Series (NPTS) algorithm is a scalable, probabilistic baseline forecaster. It uses past observations to estimate the future value distribution of a particular time series. The observed values constrain the forecasts. 

Amazon Forecast offers NPTS variations that vary in the number of historical observations sampled and how they are collected. You select a hyperparameter setting to utilise an NPTS variation.

Similar to standard forecasting systems such as ETS and ARIMA, NPTS generates individual estimates for each time series. The time series in the dataset can vary in length. The time points where the observations are available are known as the training range, and the time points where the prediction is desired are called the prediction range.

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

6️⃣ETS

Exponential Smoothing (ETS) is a popular time-series forecasting statistical algorithm. The algorithm is useful for simple datasets with fewer than 100 time series and datasets exhibiting seasonality patterns. As a forecast, ETS computes a weighted average across all observations in the time series dataset, with weights exponentially decreasing over time.

Frequently Asked Questions

How does Amazon forecasting work?

Amazon Forecast is a machine learning-based, fully managed time-series forecasting service built for business metrics research. 

What is a forecasting method?

Forecasting is a strategy that uses historical data as inputs to make well-informed predictions about future trends. Most Businesses use forecasting to determine how to allocate their budgets or plan for anticipated expenses in the future.

How does Amazon Forecast its demand?

Amazon.com employs AWS machine learning to aggregate and analyze product purchase data and run forecasting models. In addition, the corporation leverages data from browsing and purchases to make more personalized product recommendations.

Does Amazon use Amazon Forecast?

Amazon.com runs its forecasting models and collects and analyses purchasing data on products using machine learning on AWS. Additionally, it uses information about browsing and purchases to make more personalized product recommendations.

Conclusion

In this article, we learned about Amazon Forecast Algorithms. Amazon Forecast is a machine learning-based, fully managed time-series forecasting service built for business metrics research. You can also learn about Amazon Hirepro here.

We looked at six Amazon Forecast Algorithms. These are:

  1. ARIMA
     
  2. CNN-QR
     
  3. Prophet
     
  4. DeepAR+
     
  5. ETS
     
  6. NPTS
     

Click here to learn about Alexa for Business.

You can use Coding Ninjas Studio to practice various DSA questions asked in the interviews. It will help you in mastering effective coding techniques, and you will also get interview experiences with people working in big companies.

Previous article
Training Predictors of Amazon Forecast
Next article
Amazon CloudWatch
Live masterclass