Abstract: In one aspect, a computerized method for implementing multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics includes the step of collecting a set of data from various relevant providers, wherein the set of data comprises market data, events information relevant to a market, and market pricing. The method includes the step of implementing an extract, transform, load (ETL) operations on the set of data, wherein the ETL comprises the ingestion of the multi-textured data into big data storage for use on demand basis. The method includes the step of implementing one or more specified data cleaning operations on the set of data. The method includes the step of implementing one or more specified feature engineering operations on the cleaned data. The method includes the step of generating an Average daily rate (ADR) training data set. The method includes the step of generating an occupancy training data set.

Abstract: In one aspect, a computerized method for implementing a multi-layered system for heterogeneous pricing decisions by continuously learning market and hotel dynamics includes the step of collecting a set of data from relevant providers. The set of data from relevant providers comprises hotel data, competitor data, market data, and market pricing data. The method includes the step of implementing an ETL operation on the set of data from relevant providers. The ETL operation comprises an ingestion of the multi-textured data into a big data storage for use on an on-demand basis. The method includes the step of implementing one or more specified data cleaning operations on the dataset to generate a cleaned data set. The method includes the step of implementing one or more specified feature engineering operations on the cleaned data set. The method includes the step of generating an evaluation set and prediction set. The method includes the step of generating training and test dataset from the evaluation set.

Abstract: A computerized method for implementing multi-layered system for heterogeneous pricing decisions by continuously learning market and hotel dynamics comprising: collecting a set of data from various relevant providers, wherein the set of data comprises hotel data, competitor data, market data, and market pricing; implement extract, transform, load (ETL) operations on the set of data, wherein the ETL comprises the ingestion of the multi-textured data into Big data storage for use in an on-demand basis; implementing one or more specified data cleaning operations on the data-set(s).