Sold by: i4cast LLCÂ
Deployed on AWS
Free Trial
Long-Memory Vector Autoregression (LMVAR) to analyze and forecast multiple time-series influenced by common factors and hidden components.
Overview
The long-memory vector autoregressive (LMVAR) model is to analyze and forecast large number of time-series when they are influenced by both (a) evolution histories of a set of unobserved factors commonly affecting all or many time-series and (b) histories of hidden components affecting idiosyncratic components of individual time-series.
With objective data-driven constraints, the LMVAR algorithm can estimate the influences of longer histories of the unobserved factors and hidden components. The algorithm accommodates wider ranges of values of model learning parameters. The wider ranges can further enhance the power of machine learning.
Current version of the LMVAR algorithm estimates: (a) vector autoregressive (VAR) coefficients of the common components and univariate autoregressive (AR) coefficients of the idiosyncratic components, (b) implied VAR coefficients of the vector time-series, (c) forecasts of the vector time-series, and (d) impulse response to several simultaneous shocks.
Highlights
- Many social (trends, polls), sporting, economic (indicators), business (trading, sales, marketing), natural, and engineering events can be represented quantitatively by (hourly, daily, weekly, monthly, quarterly, yearly) time-series. Researches and reports reveal that (a) many of these time-series interact one another directly or though underlying common factors and (b) many time-series are influenced by their own histories as well. Vector autoregressive (VAR) model is the simplest model trying to find out predictive model of linear, mutual and temporal causality underlying these time-series.
- The long-memory vector autoregressive (LMVAR) algorithm estimates influences of longer histories of time-series or common factors with objective data-driven constraints. Without constraints, “estimated influences of long histories” can be contaminated by all kinds of random coincidences. Subjective constraints need specific assumptions which may not apply to real data sets at hand. The data-driven constraints make LMVAR accommodate wider range of values of model parameters, especially model learning parameters. The wider ranges can further enhance the power of machine learning.
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
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
Free trial
Try this product free for 120 days according to the free trial terms set by the vendor.
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.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Training Recommended | Algorithm training on the ml.m5.xlarge instance type | $0.10 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $0.10 |
ml.g4dn.4xlarge Inference (Batch) | Model inference on the ml.g4dn.4xlarge instance type, batch mode | $0.10 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $0.10 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $0.10 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.10 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $0.10 |
ml.g4dn.2xlarge Inference (Batch) | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $0.10 |
Vendor refund policy
We offer full refund for academic works. Other refunds are offered according to common practices.
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)Â .
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 algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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
LMVAR algorithm is to analyze and forecast many time-series when they are influenced by a set of unobserved factors commonly affecting all or many time-series and by hidden components affecting idiosyncratic components of individual time-series.
Current version of LMVAR algorithm estimates: (a) vector autoregressive (VAR) coefficients of the common components and univariate autoregressive (AR) coefficients of the idiosyncratic components, (b) implied VAR coefficients of the vector time-series, (c) forecasts of the vector time-series, and (d) impulse response to several simultaneous shocks.
Additional details
Inputs
- Summary
The LMVAR algorithm takes, as input data, multiple time-series data contained in a CSV (comma separated value) data table, in a format of a CSV text-string or a CSV text-file.
Each row of the data table is for values of an individual time-series (TS). Row header is the label or symbol of the time-series. Each column is for values of all time-series at a specific moment in time. Column header is the time-index or time-stamp of the moment.
- Input MIME type
- text/csv
https://github.com/i4cast/aws/blob/main/long_memory_vector_autoregressive_model/input/Weekly_VTS_6Yr.csv
https://github.com/i4cast/aws/blob/main/long_memory_vector_autoregressive_model/input/Weekly_VTS_6Yr.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Values of time stamp | Each row of the data table is for values of an individual time-series (TS). Row header is the label or symbol of the time-series. Each column is for values of all time-series at a specific moment in time. Column header is the time-index or time-stamp of the moment. The first data column is for the earliest time and the last column for the most recent time. The current version of LMVAR requires equally spaced time-stamps. | Type: FreeText | Yes |
Support
Vendor support
For questions or call-back number, please send email to i4cast LLC at prod.i4cast@gmail.com .
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
Variational Bayesian filtering Factor Analysis (VBfFA) to estimate time-varying statistical factors of large set of multiple time-series
Continuously Trained Vector Autoregressive Forecast (CTVARF) for multiple time-series influenced by common factors and hidden components.
Long-Memory Dynamic Factor Model (LMDFM) to analyze and forecast large number of time-series influenced by evolutions of unobserved factors.
This product has charges associated with it for seller support. Launch Oracle Database 19c Enterprise Edition AMI on AWS - high-performance Windows Server RDBMS with 24x7 cloudimg support.
This Ubuntu 24.04 LTS image has charges associated with it for seller support and maintenance. Ready to use minimal Ubuntu 24.04 AMI. Login using 'ubuntu' user and ssh public key authentication. Root partition and filesystem extends automatically during boot if instance volume is bigger than the default 8 GiB one. Cloud-init included. ENA enabled. Ubuntu 24.04 security updates available at the release date are included as well. Ubuntu 24.04 LTS (Noble Numbat) is a secure, high-performance Linux operating system optimized for AWS. Featuring faster boot times, reduced image size, and efficient resource usage, it's ideal for scalable cloud workloads. With Linux kernel 6.8 and the latest toolchains, it supports modern web hosting, database servers, and full-stack app development. Backed by five years of security updates, Ubuntu 24.04 delivers a reliable and developer-friendly platform for building and running cloud-native applications.
This Ubuntu 24.04 LTS image has charges associated with it for seller support and maintenance. Ready to use minimal Ubuntu 24.04 AMI automatically updated at launch with latest security patches. Login using 'ubuntu' user and ssh public key authentication. Root partition and filesystem extends automatically during boot if instance volume is bigger than the default 8 GiB one. Cloud-init included. ENA enabled. Ubuntu 24.04 LTS (Noble Numbat) is a secure, high-performance Linux operating system optimized for AWS. Featuring faster boot times, reduced image size, and efficient resource usage, it's ideal for scalable cloud workloads. With Linux kernel 6.8 and the latest toolchains, it supports modern web hosting, database servers, and full-stack app development. Backed by five years of security updates, Ubuntu 24.04 delivers a reliable and developer-friendly platform for building and running cloud-native applications.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.