English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate demand forecasting in the retail industry is a critical determinantof financial performance and supply chain efficiency. As global markets becomeincreasingly interconnected, businesses are turning towards advanced predictionmodels to gain a competitive edge. However, existing literature mostly focuseson historical sales data and ignores the vital influence of macroeconomicconditions on consumer spending behavior. In this study, we bridge this gap byenriching time series data of customer demand with macroeconomic variables,such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), andunemployment rates. Leveraging this comprehensive dataset, we develop andcompare various regression and machine learning models to predict retail demandaccurately.

Retail Demand Forecasting: A Comparative Study for Multivariate Time Series