FORECASTING AND TIME SERIES ANALYSIS-CONSULTANT

FORECASTING AND TIME SERIES ANALYSIS-CONSULTANTCIP is a not-for-profit international agricultural research organization with a global mandate to conduct research on potatoes, sweetpotatoes, Andean root and tuber crops, and sustainable management of natural resources. CIP’s vision is to contribute from its areas of expertise to the fulfillment of the Millennium Development Goals (MDGs), particularly those goals that relate to poverty, hunger, child and maternal mortality, and sustainable development. CIP has its headquarters in Lima, Peru with staff and activities in locations across Africa, Asia, and Latin America. CIP is a member of the One CGIAR, a global research partnership for a food-secure future.BackgroundWe have developed the Seed Requirement Estimation (SRE) tool to estimate the seed needed at every stage of the seed value chain. Currently, the tool relies on a series of assumptions to estimate actual seed requirements at each stage. These estimates are fully dependent on the area under cultivation for vegetatively propagated crops (VPCs). At present, we use a linear projection model, but this model lacks accuracy compared to estimation measured using Machine Learning (ML) or Artificial Intelligence (AI). Therefore, we aim to integrate machine learning and AI tools to predict the area under cultivation, production, and yield for VPCs across various market segments, including Uganda, Tanzania, and India, specifically for crops like potato, sweetpotato, and cassava.ObjectiveConduct a systematic review for the demand estimation, with particular interest in VPCs.Predict the area under cultivation for each VPC, production (tons), and yield (tons/ha) based on historical data and other factors that influence these indicators using time series analysis, ML, and AI techniques.Prepare a technical report and briefs.Work closely with the SRE digital team and assist in integrating this information into the SRE tool and validating the tool with potential users.Prepare a manuscript for a peer-reviewed journal.Conduct study design and preliminary desk study.Submit a preliminary report for comments.Prepare the final report, briefs, and manuscript.RequirementsSelection CriteriaPhD in Agribusiness, Agricultural Economics, Applied Econometrics, or a related area in time series analysis.At least eight (8) years of experience.Knowledge in forecasting and time series analysis, machine learning, artificial intelligence, ARIMA models, ARCH, and GARCH models.Excellent writing skills in English, data management and analysis skills, and the ability to interact with multidisciplinary teams from private and public partners.Time FrameConsultancy contract will be for a maximum of 30 days.Conditions: This is a national consultancy position limited to Kenyan and Ugandan nationals and permanent residents only. #J-18808-Ljbffr