Sold by: Mphasis
Deployed on AWS
The solution provides 36 months forecast of product demand using historical monthly demand data.
5
Overview
Product Demand Forecasting generates 36 months of forward forecast of the demand using historical data. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs automated model selection to apply the right model based on the input data.
Highlights
- This solution will take in monthly data as input and provide 36 months future forecast. Automatic model selection will automatically identify the set of optimal algorithms and combine their results using ensemble learning to provide the results.
- Mphasis Time Series Forecasting can be applied in product demand forecasting.
- Need customized Deep Learning and Machine Learning solutions? Get in touch!
Details
Sold by
Categories
Delivery method
Latest version
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $10.00 |
ml.m5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.2xlarge instance type, real-time mode | $5.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $10.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $10.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $10.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $10.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $10.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $10.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $10.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Input
• Supported content types: text/csv • Sample input file: (https://tinyurl.com/yasf5yld )
maskedsku 2015-04-04 F0007 1338 Input should have - Have an unique identifier column called 'maskedsku'. eg. maskedsku can be your shipment id.
- The date format of the columns should be: 'YYYY-MM-DD'
Output
• Content type: text/csv • Sample output file:(https://tinyurl.com/ybtv3brd )
maskedsku 2015-04-04 20211101_forecast F0007 1338 1894 Invoking endpoint
AWS CLI Command
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
!aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$file_name --content-type 'text/csv' --region us-east-2 result.csvSubstitute the following parameters:
- "endpoint-name" - name of the inference endpoint where the model is deployed
- file_name - input csv name
- text/csv - MIME type of the given input
- result.csv - filename where the inference results are written to.
Resources
- Input MIME type
- text/csv
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
Deploy Amazon Chronos 2, the 120M-parameter time series foundation model, with zero-shot forecasting that eliminates months of model training. Our AWS Premier Tier consulting team delivers end-to-end implementation, from data pipelines and SageMaker/Bedrock deployment to production monitoring, all in days, not months. Chronos 2 outperforms traditional methods like ARIMA and Prophet with 90%+ win rates in accuracy benchmarks while handling cold-start scenarios, multivariate predictions, and 300+ forecasts per second on a single GPU.
This solution provides 30 weeks of forecast of passengers who are expected to travel using historical weekly passenger traffic data.
The solution provides 30 months forecast of retail sales using historical monthly sales data.
The solution provides 30 months forecast of Energy Consumption using historical monthly Energy Consumption data.
Customer reviews
Madhura D.
Time Series graph or method elementary way to forecast the demand for products
Reviewed on Jul 27, 2023
Review provided by G2
What do you like best about the product?
It helps to forecast the future demand of products in the market, what product could be bought by people or what product to scrap away
What do you dislike about the product?
Sometimes acts as a complex to find what will be the demand of the product
What problems is the product solving and how is that benefiting you?
It relies on historical data and assumes if the historical data is the good indicator to forecast the future. It is predicting future events by analyzing past trends, so helping by forecasting the future