I$\text{'}$m working on a time series analysis project for stock price prediction using LSTM $($Long Short$-$Term Memory$)$ neural networks. Here are the details:

Dataset characteristics:

$-$ Time period: $118$ months of historical data

$-$ Features: Two columns

$*$ month: time sequence $($monthly$)$

$*$ close: monthly closing stock prices

My approach:

$1.$ Plan to use all data for training

$2.$ Evaluate using $10-$fold cross$-$validation

$3.$ Predict the next $6$ months of stock prices

Questions:

$1.$ Is my dataset size $(118$ months$)$ sufficient for LSTM implementation in stock price prediction?

$2.$ What would be the most appropriate way to:

$-$ Basic data preprocessing

$-$ Split the data

$-$ Structure the LSTM model

$-$ Validate the results

$3.$ Is $10-$fold cross$-$validation suitable for this time series data?

$4.$ What would be the recommended sequence length $($look$-$back period$)$ for the LSTM model?

$5.$ How can I improve the model's accuracy for $6-$month ahead predictions?

Please provide:

$-$ Suggested code structure

$-$ Best practices for stock price prediction using LSTM

$-$ Alternative validation methods if $10-$fold cross$-$validation isn't suitable

$-$ Recommendations for feature engineering with this limited dataset