Instead of using k-fold cross-validation, you should be aware to the fact that atime series is not randomly distributed data - It is inherently ordered bychronological order.
In case of time series data, you should use techniques like forward chaining –Where you will be model on past data then look at forward-facing data.
fold 1: training[1], test[2]
fold 1: training[1 2], test[3]
fold 1: training[1 2 3], test[4]
fold 1: training[1 2 3 4], test[5]

Logistic Regression often referred as logit model is a technique to predict thebinary outcome from a linear combination of predictor variables. For example, ifyou want to predict whether a particular political leader will win the election ornot. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). Thepredictor variables here would be the amount of money spent for electioncampaigning of a particular candidate, the amount of time spent in campaigning,etc.

Deep learning is subfield of machine learning inspired by structure and functionof brain called artificial neural network. We have a lot numbers of algorithmsunder machine learning like Linear regression, SVM, Neural network etc anddeep learning is just an extention of Neural networks. In neural nets we consider small number of hidden layers but when it comes to deep learning algorithms we consider a huge number of hidden latyers to better understand the input outputrelationship.

Machine learning:
Machine learning is a field of computer science that gives computers the abilityto learn without being explicitly programmed. Machine learning can becategorized in following three categories.
1. Supervised machine learning,
2. Unsupervised machine learning,
3. Reinforcement learning

Deep learning:
Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

Reinforcement Learning is learning what to do and how to map situations toactions. The end result is to maximize the numerical reward signal. The learneris not told which action to take, but instead must discover which action will yieldthe maximum reward.Reinforcement learning is inspired by the learning ofhuman beings, it is based on the reward/panelity mechanism.