Feature ml models

docs/machine_learning_models_documentation.md

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+# Machine-Learning Models Documentation
+
+## General Usage
+[Hybrid Model]
+
+The following example shows the high-level entry to training and forecasting using the function train_hybrid_model() of the hybrid-model.
+```python
+    file = os.path.join(os.path.dirname(__file__), "../examples/tmio/JSONL/8.jsonl")
+    model = train_hybrid_model(file, epochs=10, lr=0.003)
+    prediction = predict_next_sequence(model, file)
+```
+The function train_hybrid_model() also has parameters with standard values for the underlying structure of the model which can be changed.
+In this example only the embedded dimension is changed, but there are also parameters for the attention heads, the feed-forward dimension etc.
+Common values such as 2^n are usually the most effective variations to explore. 
+```python
+    file = os.path.join(os.path.dirname(__file__), "../examples/tmio/JSONL/8.jsonl")
+    model = train_hybrid_model(file, epochs=10, lr=0.003, emb_dim = 256)
+    prediction = predict_next_sequence(model, file)
+```
+The training of the hybrid-model can be resumed by loading a .pth file created by the saving process.
+It contains the parameters of the model and the state of the used optimizer. 
+```python
+    file = os.path.join(os.path.dirname(__file__), "../examples/tmio/JSONL/8.jsonl")
+    model = train_hybrid_model(file, epochs=10, lr=0.003, save=True)
+    model = train_hybrid_model(
+        file,
+        epochs=10,
+        lr=0.003,
+        load_state_dict_and_optimizer_state="model_and_optimizer.pth",
+    )
+    prediction = predict_next_sequence(model, file)
+```
+[(S)ARIMA]
+
+The following example shows the high-level entry to training and forecasting using the train_arima() function of the ARIMA/SARIMA models.
+By changing the model_architecture parameter SARIMA or ARIMA can be selected. The max_depth is recommended to be relatively small, 
+since it's defining the maximum depth of differentations of the underlying data to reach stationarity.
+A resumption of training is inherently not supported by the underlying model structure. Therefore, if new data is available, then 
+training from the beginning is the only option.
+```python
+    file = os.path.join(os.path.dirname(__file__), "../examples/tmio/JSONL/8.jsonl")
+    prediction = train_arima(file, max_depth = 3, model_architecture="ARIMA")
+```