A time series is an ordered collection of random variables. Considering a one-dimensional time series $A_i = {a_{i1},a_{i2},\ldots,a_{it}}$ where $t$ denotes the time index. So, the time series is a sequence $\{a\}_{j=1}^t$. Let, $B = \{A_1,A_2,\ldots,A_N\}$ be N time series.

An image consists of pixels and characterized by features. Let the feature vector $f_{object} = {f_1,f_2,\ldots,f_d}$ for a $d$ dimensional image. If there are multiple images, say N different images then for each image object there will be a feature set $F = \{f_{object1}, f_{object1}, \ldots, f_{objectN}\} $. Let the feature vector set be a N by d matrix.

My question is, whether the feature vector for each object $f_{object}$ is a time-series OR is the set $F$ that is consisting of a matrix N by d a d dimensional time series. Is each feature vector for an object considered as a d dimensional time-series or are there d time series and then the dimension of the time series is N ?

A time series is an ordered collection of random variables. Considering a one-dimensional time series $A_i = {a_{i1},a_{i2},\ldots,a_{it}}$ where $t$ denotes the time index. So, the time series is a sequence $\{a\}_{j=1}^t$. Let, $B = \{A_1,A_2,\ldots,A_N\}$ be N time series.

An image consists of pixels and characterized by features. Let the feature vector $f_{object} = {f_1,f_2,\ldots,f_d}$ for a $d$ dimensional image. If there are multiple images, say N different images then for each image object there will be a feature set $F = \{f_{object1}, f_{object1}, \ldots, f_{objectN}\} $. Let the feature vector set be a N by d matrix.

My questions are,

(1) Whether the feature vector for each object $f_{object}$ is a time-series, OR

is the set $F$, that is consisting of a matrix N by d, a d dimensional time series?

(2) Is each feature vector for an object considered as a d dimensional time-series or are there d time series and then the dimension of the time series is N ?