Multivariate Time Series Forecasting
52 papers with code • 7 benchmarks • 8 datasets
Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
Probabilistic forecasting, i. e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes.
Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.
To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by recurrent neural network (RNN) with attention mechanism.