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Finish pred README with NN config

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DidierRLopes
2021-02-20 19:42:44 +00:00
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prediction_techniques/README.md
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@@ -30,8 +30,9 @@ This menu aims to predict the share price of a pre-loaded stock, and the usage o
## sma <a name="sma"></a>
```
usage: simple_moving_average [-l N_LENGTH] [-d N_DAYS]
usage: sma [-l N_LENGTH] [-d N_DAYS]
```
Simple Moving Average:
* -l : length of SMA window. Default 20.
* -d : prediciton days. Default 5.
@@ -41,6 +42,7 @@ usage: simple_moving_average [-l N_LENGTH] [-d N_DAYS]
```
usage: knn [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS] [-n N_NEIGHBORS]
```
k-Nearest Neighbors:
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
@@ -53,6 +55,7 @@ usage: knn [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS] [-n N_NEIGHBORS]
usage: linear [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: regression -p 1 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
```
Linear Regression (p=1):
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
@@ -64,6 +67,7 @@ usage: regression -p 1 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: quadratic [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: regression -p 2 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
```
Quadratic Regression (p=2):
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
@@ -75,6 +79,7 @@ usage: regression -p 2 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: cubic [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
usage: regression -p 3 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
```
Cubic Regression (p=3):
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
@@ -85,6 +90,7 @@ usage: regression -p 3 [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
```
usage: regression -p N_POLYNOMIAL [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
```
Regression:
* -i : number of days to use for prediction. Default 40.
* -d : prediciton days. Default 5.
* -j : number of jumps in training data. Default 1.
@@ -96,6 +102,7 @@ usage: regression -p N_POLYNOMIAL [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS]
```
usage: arima [-d N_DAYS] [-i {aic,aicc,bic,hqic,oob}] [-s] [-r] [-o S_ORDER]
```
Auto-Regressive Integrated Moving Average:
* -d : prediciton days. Default 5.
* -i : information criteria - used if auto_arima library is invoked. Default aic.
* -s : weekly seasonality flag. Default False.
@@ -111,6 +118,7 @@ usage: arima [-d N_DAYS] [-i {aic,aicc,bic,hqic,oob}] [-s] [-r] [-o S_ORDER]
```
usage: fbprophet [-d N_DAYS]
```
Facebook's Prophet:
* -d : prediciton days. Default 5.
![prophet](https://user-images.githubusercontent.com/25267873/108604938-cf9ffa00-73a8-11eb-973b-0affb343e2f6.png)
@@ -120,6 +128,7 @@ usage: fbprophet [-d N_DAYS]
usage: mlp [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [-e N_EPOCHS] [-p {normalization,standardization,none}]
[-o {adam,adagrad,adadelta,adamax,ftrl,nadam,optimizer,rmsprop,sgd}] [-l {mae,mape,mse,msle}]
```
MulitLayer Perceptron:
* -d : prediciton days. Default 5.
* -i : number of days to use for prediction. Default 40.
* -j : number of jumps in training data. Default 1.
@@ -128,6 +137,21 @@ usage: mlp [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [-e N_EPOCHS] [-p {normalizati
* -o : optimization technique. Default adam.
* -l : loss function. Default mae.
Due to the complexity of defining a model through command line, one can define it in: [config_neural_network_models.txt](/config_neural_network_models.py)
```
MultiLayer_Perceptron \
= [ {'Dense':
{'units':50, 'activation':'relu'} },
{'Dense':
{'units':100, 'activation':'relu'} },
{'Dense':
{'units':80, 'activation':'relu'} },
{'Dense':
{'units':30, 'activation':'relu'} },
{'Dense':
{'activation':'linear'} }]
```
![mlp](https://user-images.githubusercontent.com/25267873/108604944-d2025400-73a8-11eb-9ab6-52972160cd2a.png)
## rnn <a name="rnn"></a>
@@ -135,6 +159,7 @@ usage: mlp [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [-e N_EPOCHS] [-p {normalizati
usage: rnn [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [-e N_EPOCHS] [-p {normalization,standardization,none}]
[-o {adam,adagrad,adadelta,adamax,ftrl,nadam,optimizer,rmsprop,sgd}] [-l {mae,mape,mse,msle}]
```
Recurrent Neural Network:
* -d : prediciton days. Default 5.
* -i : number of days to use for prediction. Default 40.
* -j : number of jumps in training data. Default 1.
@@ -143,6 +168,21 @@ usage: rnn [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [-e N_EPOCHS] [-p {normalizati
* -o : optimization technique. Default adam.
* -l : loss function. Default mae.
Due to the complexity of defining a model through command line, one can define it in: [config_neural_network_models.txt](/config_neural_network_models.py)
```
Recurrent_Neural_Network \
= [ {'SimpleRNN':
{'units':100, 'activation':'linear', 'return_sequences':True} },
{'SimpleRNN':
{'units':50, 'activation':'linear', 'return_sequences':True} },
{'Dropout':
{'rate':0.2} },
{'SimpleRNN':
{'units':21, 'activation':'linear', 'return_sequences':False} },
{'Dense':
{'activation':'linear'} }]
```
![rnn](https://user-images.githubusercontent.com/25267873/108604940-d0d12700-73a8-11eb-837e-a5aa128942d9.png)
## lstm <a name="lstm"></a>
@@ -150,6 +190,7 @@ usage: rnn [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [-e N_EPOCHS] [-p {normalizati
usage: lstm [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [-e N_EPOCHS] [-p {normalization,standardization,none}]
[-o {adam,adagrad,adadelta,adamax,ftrl,nadam,optimizer,rmsprop,sgd}] [-l {mae,mape,mse,msle}]
```
Long-Short Term Memory:
* -d : prediciton days. Default 5.
* -i : number of days to use for prediction. Default 40.
* -j : number of jumps in training data. Default 1.
@@ -158,6 +199,17 @@ usage: lstm [-d N_DAYS] [-i N_INPUTS] [-j N_JUMPS] [-e N_EPOCHS] [-p {normalizat
* -o : optimization technique. Default adam.
* -l : loss function. Default mae.
Due to the complexity of defining a model through command line, one can define it in: [config_neural_network_models.txt](/config_neural_network_models.py)
```
Long_Short_Term_Memory \
= [ {'LSTM':
{'units':25, 'activation':'tanh', 'return_sequences':True} },
{'LSTM':
{'units':15, 'activation':'tanh', 'return_sequences':False} },
{'Dense':
{'activation':'linear'} }]
```
![lstm](https://user-images.githubusercontent.com/25267873/108604943-d2025400-73a8-11eb-83c5-edb4a2121cba.png)