The correct answer is D. Long Short-Term Memory (LSTM).
LSTM is a type of recurrent neural network (RNN) that is commonly used for time series forecasting and trend analysis. RNNs are well-suited for tasks that involve sequential data, such as time series data. LSTMs are a type of RNN that have been specifically designed to handle long-term dependencies in data. This makes them well-suited for tasks such as forecasting and trend analysis, which often involve data that is highly correlated over time.
K-Nearest Neighbors (KNN), Decision Tree, and Linear Regression are all machine learning algorithms that can be used for a variety of tasks, but they are not as well-suited for time series forecasting and trend analysis as LSTMs. KNN is a supervised learning algorithm that works by finding the k nearest neighbors of a given data point and then using the labels of those neighbors to predict the label of the data point. Decision Trees are a type of supervised learning algorithm that work by recursively splitting data into smaller and smaller subsets until each subset contains only one class. Linear Regression is a type of supervised learning algorithm that works by finding a linear relationship between the input features and the output label.
While KNN, Decision Tree, and Linear Regression can be used for time series forecasting and trend analysis, they are not as accurate as LSTMs. This is because LSTMs are specifically designed to handle long-term dependencies in data, which are common in time series data. KNN, Decision Tree, and Linear Regression are not as well-suited for handling long-term dependencies, which can lead to inaccurate predictions.