Understanding epoch, batch size, accuracy ,performance gain in lstm forecasting model

I am new to machine learning and lstm. I am referring this link LSTM for multistep forecasting for Encoder-Decoder LSTM Model With Multivariate Input section.

Here is my dataset description after reshaping the train and test set.

print(dataset.shape)
print(train_x.shape, train_y.shape)
print((test.shape)

(2192, 15)
(1806, 14, 14) (1806, 7, 1)
(364, 15)


In above I have n_input=14, n_out=7.

Here is my lstm model description:

def build_model(train, n_input):
# prepare data
train_x, train_y = to_supervised(train, n_input)
# define parameters
verbose, epochs, batch_size = 2, 100, 16
n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]
# reshape output into [samples, timesteps, features]
train_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 1))
# define model
model = Sequential()
# fit network
model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose)
return model


On evaluating the model, I am getting the output as:

Epoch 98/100
- 8s - loss: 64.6554
Epoch 99/100
- 7s - loss: 64.4012
Epoch 100/100
- 7s - loss: 63.9625


According to my understanding: (Please correct me if I am wrong)

Here my model accuracy is 63.9625 (by seeing the last epoch 100). Also, this is not stable since there is a gap between epoch 99 and epoch 100.

Here is my some basic doubt:

1) Please suggest to me how epoch and batch size above defined is related to gaining model accuracy. How its increment and decrement affect model accuracy?

2) Is my above-defined epoch, batch, n_input is correct for the model?

3) How I can increase my model accuracy. Is the above dataset size is good enough for this model?

Please suggest me as I am not able to link all this parameter and kindly help me in understanding how to achieve more accuracy by the above factor. Thanks!!