Increment for adjusting the series to account for seasonality

Indicator for the moving average component

Integration process to achieve stationarity by differencing the time series

Independence of residuals in the error term

It models the relationship between the current value and past values.

It captures the autocorrelation in the time series data

It is most useful for series that exhibit strong trends.

It uses lagged values of the series to predict future values

Finding Itemsets for future trends

Same as frequent itemset mining

Finding of strong association rules using frequent itemsets

Using association to analyze correlation rules

Instance segmentation does not use CNNs

Semantic segmentation assigns a class to each pixel, instance segmentation also distinguishes object instances

Semantic segmentation is only used for medical imaging

Instance segmentation detects only one class, while semantic handles all

Support(A U B) / Support (B)

Support(A ∩ B) / Support (A)

Support(A ∩ B) / Support (B)

Support(A U B) / Support (A)

(y - y`)2 / 2N

(y - y`) / 2N

(y - y`)

(y - y`) / N

Replace missing values with the mode (most frequent value) of the column

Replace missing values with the mean of the column.

Replace missing values with random noise

Impute missing values using linear regression

Model deployment

Data cleaning

Model evaluation

Feature engineering

Data aggregation

Data storage

Data normalization

Data visualization

Intervals

Quantiles

Range

Whiskers