diff --git a/aeon/classification/distance_based/_proximity_forest.py b/aeon/classification/distance_based/_proximity_forest.py index 44871a2500..40fca3624d 100644 --- a/aeon/classification/distance_based/_proximity_forest.py +++ b/aeon/classification/distance_based/_proximity_forest.py @@ -44,7 +44,7 @@ class ProximityForest(BaseClassifier): n_jobs : int, default = 1 The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` + ``-1`` means using all processors. for more details. Parameter for compatibility purposes, still unimplemented. parallel_backend : str, ParallelBackendBase instance or None, default=None Specify the parallelisation backend implementation in joblib, if None a 'prefer' diff --git a/aeon/classification/distance_based/_time_series_neighbors.py b/aeon/classification/distance_based/_time_series_neighbors.py index d0f7144ebe..40d65f9a47 100644 --- a/aeon/classification/distance_based/_time_series_neighbors.py +++ b/aeon/classification/distance_based/_time_series_neighbors.py @@ -49,7 +49,7 @@ class KNeighborsTimeSeriesClassifier(BaseClassifier): n_jobs : int, default = None The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` + ``-1`` means using all processors. for more details. Parameter for compatibility purposes, still unimplemented. Examples diff --git a/aeon/regression/distance_based/_time_series_neighbors.py b/aeon/regression/distance_based/_time_series_neighbors.py index 5c065939f9..ed70031112 100644 --- a/aeon/regression/distance_based/_time_series_neighbors.py +++ b/aeon/regression/distance_based/_time_series_neighbors.py @@ -49,7 +49,7 @@ class KNeighborsTimeSeriesRegressor(BaseRegressor): n_jobs : int, default = None The number of parallel jobs to run for neighbors search. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. - ``-1`` means using all processors. See :term:`Glossary ` + ``-1`` means using all processors. for more details. Parameter for compatibility purposes, still unimplemented. Examples diff --git a/docs/glossary.md b/docs/glossary.md deleted file mode 100644 index f660adadaf..0000000000 --- a/docs/glossary.md +++ /dev/null @@ -1,191 +0,0 @@ -# Glossary of Common Terms - -The glossary below defines common terms and API elements used throughout `aeon`. - -```{glossary} -:sorted: - -Time series data -Time series -Series - Data with multiple individual {term}`variable` measurements with accompanying - {term}`timepoints` which are ordered over time or have an index indicating the - position of an observation in the sequence of values. - -Timepoint -Timepoints - The point in time that an observation is made for a {term}`time series`. A time - point may represent an exact point in time (a timestamp), a time period (e.g. - minutes, hours or days), or simply an index indicating the position of an - observation in the sequence of values. - -Variable -Variables - Refers to some measurement of interest. Variables may be singular values - (e.g. time-invariant measurements like a patient's place of birth) or a sequence - of multiple values as a {term}`time series`. - - For time series data, multiple variables may be referred to as {term}`channels`. - -Target variable -Target variables - The {term}`variable`(s) to be predicted in a learning task using - {term}`Independent variables`, past {term}`timepoints` of the variable itself, or - both. Also referred to as the dependent or endogenous variable(s). - -Independent variable -Independent variables - The {term}`variable`(s) that are used to predict the {term}`target variable`(s) - in a learning task. Also referred to as exogenous variables Commonly also known as - features and attributes in traditional machine learning settings. - -Channel -Channels - A channel is a singular {term}`time series` in a data set which contains multiple - time series {term}`variables`. A dataset with multiple channels is - {term}`multivariate`. - -Time series machine learning - A general term for using machine learning algorithms to learn predictive models - from {term}`time series` data. `aeon` is a library for time series machine learning - algorithms. - -Forecasting - A {term}`Time series machine learning` task focused on prediction future values of - a {term}`time series`. - -Time series classification - A learning task focused on using the patterns across {term}`instances` between the - {term}`time series` and a categorical {term}`target variable`. - -Time series regression - A learning task focused on using learning patterns from multiple {term}`time series` - and a continuous {term}`target variable`. There are two related but distinct - learning tasks that fall under this category: {term}`time series forecasting - regression` and {term}`time series extrinsic regression`. - -Time series forecasting regression - This learning relates to {term}`forecasting` {term}`reduced ` to - regression through a sliding window. This is the more familiar type of regression - in literature. - -Time series extrinsic regression - A learning task focused on using the patterns across {term}`instances` between the - {term}`time series` and a continuous {term}`target variable`. The `aeon` - `regression` module is focused on this type of regression. - -Time series clustering - A learning task focused on discovering groups consisting of {term}`instances` with - similar {term}`time series`. - -Time series annotation - A collection of learning tasks focused on labelling the {term}`variables` of a - {term}`time series`. This includes the related tasks of anomaly detection, change - point detection and segmentation. - -Time series transformation -Time series transformers - Transformers usually refers to classes in the `transformation` module of `aeon`. - These classes are used to transform {term}`time series` data into a different - format. This may be to reduce the dimensionality of the data, to extract features - from the data, or to transform the data into a different format. - - See {term}`series-to-series transformation` and {term}`series-to-features - transformation` for types of transformer. - -Time series similarity search - A task focused on finding the most similar candidates to a given - {term}`time series` of length `l`, called the query. The candidates are - extracted from a collection of {term}`time series` of length equal or - superior to `l`. - -Collection transformers - {term}`Time series transformers` that take a {term}`time series collection` as - input. While these transformers only accept collections, a wrapper is provided to - allow them to be used with singular time series datatypes. - -Series-to-series transformation - {term}`Time series transformers` that take a {term}`time series` as input and - output a (different) time series. An example of this is the Discrete - Fourier Transform (DFT). - -Series-to-features transformation - {term}`Time series transformers` that take a {term}`time series` as input and - output a set of features (in {term}`tabular` format for {term}`time series - collections`. An example of this is the extraction of the mean and various other - summary statistics from the series. - -Instances -Instance - A member of the set of entities being studied and which an machine learning - practitioner wishes to generalize. For example, patients, chemical process runs, - machines, countries, etc. - - May also be referred to as cases, samples, examples, observations or records - depending on the discipline and context. - -Panel -Time series panel - Common alternative name for {term}`time series collection`. - -Time series collection -Time series collections - A datatype which contains multiple {term}`instances` of time series. These series - may be {term}`univariate time series` or {term}`multivariate time series`. The time - series contained within may be of different lengths, sampled at different - frequencies, contain differing {term}`timepoints` etc. - - Also referred to as a {term}`panel time series` depending on context and discipline. - -Univariate -Univariate time series - A single {term}`time series`. - -Multivariate -Multivariate time series - A {term}`time series` with multiple {term}`channels`. Typically observed for the - same observational unit. Multivariate time series is typically used to refer to - cases where the series evolve together over time. - - An example of time series data with multiple channels is data extracted from a - gyroscope sensor, which can produce different time series data for the x, y and - z axes of the device. - -Reduction - Reduction refers to decomposing a given learning task into simpler tasks that can - be composed to create a solution to the original task. In `aeon` reduction is used - to allow one learning task to be adapted as a solution for an alternative task. - -Trend - When time series show a long-term increase or decrease, this is referred to as a - trend. Trends can also be non-linear. - -Seasonality - When a {term}`time series` is affected by seasonal characteristics such as the time - of year or the day of the week, it is called a seasonal pattern. - The duration of a season is always fixed and known. - -Tabular - A 2 dimensional data structure where the rows of the matrix represent { - term}`instances` and the columns represent {term}`variables`. This is the most - common data structure used in `scikit-learn`. - - A {term}`univariate time series` can be formatted in this way, where each - variable of being measured for each instance are treated as - features and stored as a primitive data type in the 2d data structure. E.g., there - are N instances of time series and each has T {term}`timepoint`, this would yield - a matrix with shape (N, T): N rows, T columns. - -random_state - A parameter for controlling random number generation in estimators and functions. - Follows the conventions of [scikit-learn](https://scikit-learn.org/stable/glossary.html#term-random_state). - - If `int`, random_state is the seed used by the random number generator; - If `RandomState` instance, random_state is the random number generator; - If `None`, the random number generator is the `RandomState` instance used by - `np.random`. - -n_jobs - A parameter for controlling the number of threads used in estimators. - Follows the conventions of [scikit-learn](https://scikit-learn.org/stable/glossary.html#term-n_jobs). -```