We categorize the features of a timeserie prediction problem into three aspects:
- global covariates: covariates that are indepedent of the time series themselves, such as time features, time series covariates (e.g., to predict time series
$y_1$ , we might use$y_2$ as a covariate).-
time_features
: time features based on time series data time index. -
cat_cov_global
: categorical global features other than time features such as external features like quarterly financial report status if we want to include. Here we dont have any such features in M5. -
num_cov_global
: numerical global features other than time features such as Dowjones index if we want to include. Here we dont have any such features in M5.
-
- local covariates: covariates that are dependent on the time series themselves, such as the attributes of the time series (e.g., index of the series in a multi-variate prediction problem, store_id of the product prices etc).
-
cat_cov_local_variant
: local time-variant categorical covariates, whose raw data shape is$(N, M)$ , such as the SNAP status of the state the store is located, etc. -
num_cov_local_variant
: local time-variant numerical covariates, whoe raw data shape is$(N,M)$ , such as the price of an item. -
cat_cov_local_invariant
: local time-invariant categorical covariates, whose raw data shape is$(M, L_c)$ , where$M$ is number of time series,$L_c$ is the number of time-invariant categorical covariates. In M5, this could be theid
,store_id
,state_id
for each time series. -
num_cov_local_invariant
: local time-invariant numerical covariates, whose raw data shape is$(M, L_n)$ .
-
We design these features so that we can use time-windows to generate look-backs and prediction horizons of time_features
, cat_cov_global
, num_cov_global
, cat_cov_local_variant
, and num_cov_local_variant
.
The batch design of a data loader has two different approaches:
- batch on time steps: e.g., the time series window time could then be
$(B, M, L+H)$ , where$B$ is a batch size, and the batch is taken on time step index,$M$ is the number of time series to be predicted,$L$ is the history length,$H$ is the prediction length. - batch on number of time series: the time series window could then be
$(B, L+H)$ , where$B$ is taken on$M$ , thus$B <= M$ .