First, the task you are currently dealing with is multivariate Time Series Prediction (MTSF), which belongs to the generative category of tasks. I attempted to transfer your entire framework to ...

Learn how transformer-based models like Chronos and PatchTST are revolutionizing predictive analytics across industries.

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Compared to standard linear ...

This paper focuses on a multidimensional indicator prediction assessment method based on time series and multiple linear regression modelling. Through in-depth analysis of relevant data, principal ...

For time series analysis, simple linear regression is helpful, but advanced techniques can enhance predictions: Lagged Variables: Incorporate past values to account for historical influence.

Therefore, this paper proposes to combine granular computing theory with support vector machines to achieve large-sample time series data prediction. Firstly, the definition of time series is analyzed ...

Summary We apply an interpretable Long Short-Term Memory (iLSTM) network for land-atmosphere carbon flux predictions based on time series observations of seven environmental variables. iLSTM enables ...

Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series Time series forecasting plays a key role in many fields such as business, energy or environment.