PP: Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting

Problem: high-dimensional time series forecasting

?? what is "high-dimensional" time series forecasting?

one dimension for each individual time-series. n个time series为n维。 

A need for exploiting global pattern and coupling them with local calibration校准 for better prediction. 

However, most are one-dimensional forecasting.

one-dimensional forecasting VS high-dimensional forecasting:

1. a single dimension forecast mainly depends on past values from the same dimension. 

DeepGLO: a deep forecasting model which thinks globally and acts locally.

A hybrid model: a global matrix factorization model regularized by a temporal convolution network + a temporal network that capture local properties of each time-series and associated covariates相关协变量.

Environment: different time series can have vastly different scales without a priori normalization or rescaling.  

Introduction:

需求:比如零售商,one may be interested in the future daily demands for all items in a category. This leads to a problem of forecasting n time-series.

Traditional methods: focus on one time-series or a small number of time-series at a time. 

AR, ARIMA, exponential smoothing and so on. 

?? how to share temporal patterns in the whole data-set while training and prediction?

RNN - sequential modeling; and suffer from the gradient vanishing/ exploding problems. 

LSTM 解决了上述问题。

Wavenet model: temporal convolutions/ causal convolutions. 

Temporal convolution has been recently used, however, they still have two important shortcomings:

1. hard to train on data-sets that have wide variation in scales.  

2. even though these deep models are trained on the entire data-set, during prediction the models only focus on local past data. i.e only the past data of a time-series is used for predicting the future of that time-series.

global properties. take in multiple time-series in the input layer thus capturing global properties. 

 

PP: Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting

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