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There could be several reasons why TensorFlow's LSTM RNN encounters difficulties in making accurate predictions, even though the data includes the target variable. Some possible reasons are:

1. Insufficient amount of data: LSTM RNNs require a significant amount of data to be able to learn and generalize patterns effectively. If the dataset is small or incomplete, the LSTM RNN may have difficulty inferring complex patterns and making accurate predictions.

2. Lack of diversity in data: LSTM RNNs need a diverse dataset that covers a wide range of scenarios to learn the patterns effectively. If the data is too homogeneous, the LSTM RNN may not be able to capture the complexities of the data and make accurate predictions.

3. Poor quality of the data: If the data has missing or unreliable information, the LSTM RNN will have difficulty learning accurate patterns from it. In such cases, data cleaning and preprocessing are necessary to ensure that the data is of high quality.

4. Overfitting: If the LSTM RNN is trained for an extended period and is unable to generalize the patterns effectively, it may start overfitting the data. Overfitting occurs when the LSTM RNN only learns the patterns specific to the training data and is unable to generalize to new data, causing a drop in prediction accuracy.

5. Incorrect model architecture: The choice of an inappropriate model architecture can also lead to lower prediction accuracy. In many cases, a different architecture, such as a different type of RNN or a different optimization algorithm, may be more suitable for the problem at hand.