Multivariate Time Series Forecasting
Budget: $30 – $250 USD
We are seeking an experienced Machine Learning Engineer. The project involves predicting 19 dependent climate variables, including temperature and humidity, which are collected from a climate-controlled environment. The predictions will be based on 28 independent variables, with the aim of forecasting the dependent variables at various time horizons, ranging from 1 to 8 hours.
The ideal candidate will leverage their expertise in Long Short-Term Memory (LSTM) networks, or similar technologies, to handle this multivariate time-series forecasting task. The data set consists of over 500,000 observations gathered over a span of more than a year. The input to NN has to be editable enabling the change in the last observations before prediction. The project has to contain documentation on evaluation metrics and their interpretation.
The ideal candidate will leverage their expertise in Long Short-Term Memory (LSTM) networks, or similar technologies, to handle this multivariate time-series forecasting task. The data set consists of over 500,000 observations gathered over a span of more than a year. The input to NN has to be editable enabling the change in the last observations before prediction. The project has to contain documentation on evaluation metrics and their interpretation.