imbrium 0.1.2

Standard and Hybrid Deep Learning Multivariate-Multi-Step & Univariate-Multi-Step Time Series Forecasting.

Imbrium

Pip install

pip install imbrium

Standard and Hybrid Deep Learning Multivariate-Multi-Step (+ Univariate-Multi-Step, because why not?) Time Series Forecasting.

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Basics

This library aims to ease the application of deep learning models for time series forecasting. To achieve this, the library differentiates between two modes:

1. Univariate-Multistep forecasting
2. Multivariate-Multistep forecasting

These two main modes are further divided based on the complexity of the underlying model architectures:

1. Standard
2. Hybrid

Standard supports the following architectures:

• Multilayer perceptron (MLP)
• Recurrent neural network (RNN)
• Long short-term memory (LSTM)
• Gated recurrent unit (GRU)
• Convolutional neural network (CNN)
• Bidirectional recurrent neural network (BI-RNN)
• Bidirectional long-short term memory (BI-LSTM)
• Bidirectional gated recurrent unit (BI-GRU)
• Encoder-Decoder recurrent neural network
• Encoder-Decoder long-short term memory
• Encoder-Decoder convolutional neural network (Encoding via CNN, Decoding via GRU)
• Encoder-Decoder gated recurrent unit

Hybrid supports:

• Convolutional neural network + recurrent neural network (CNN-RNN)
• Convolutional neural network + Long short-term memory (CNN-LSTM)
• Convolutional neural network + Gated recurrent unit (CNN-GRU)
• Convolutional neural network + Bidirectional recurrent neural network (CNN-BI-RNN)
• Convolutional neural network + Bidirectional long-short term memory (CNN-BI-LSTM)
• Convolutional neural network + Bidirectional gated recurrent unit (CNN-BI-GRU)

Please note that each model is supported by a prior input data pre-processing procedure which allows to set how many datapoints should look a model look back for a prediction, how many datapoints should be predicted into the future, how many sub-sequences should be considered (only for hybrid architectures) and what scaling should be applied.

The following scikit-learn scaling procedures are supported:

• StandardScaler
• MinMaxScaler
• MaxAbsScaler
• Normalizing ([0, 1])
• None (raw data input)

Trained models can furthermore be saved or loaded if the user wishes to do so.

How to use Imbrium?

Simplified workflows, more possible.

Univariate Models:

1. Univariate-Multistep forecasting - Standard architectures

from imbrium.predictors.univarstandard import *

predictor = BasicMultStepUniVar(steps_past: int, steps_future: int, data = pd.DataFrame(), scale: str = '')

# Choose between one of the architectures:

# predictor.create_mlp(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_rnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_lstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_gru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_birnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_bilstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_bigru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_encdec_rnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_encdec_lstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_encdec_cnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_encdec_gru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')

# Fit the predictor object
predictor.fit_model(epochs: int, show_progress: int = 1, validation_split: float = 0.20, batch_size: int = 10)

# Have a look at the model performance - MSE based, more evaluation forms might be added on architecture level in the future
predictor.show_performance()

# Make a prediction based on new unseen data
predictor.predict(data: array)

# Safe your model:
predictor.save_model()

# Load a model:
# Step 1: initialize a new predictor object with same characteristics as model to load
# Step 2: Do not pass in any data
# Step 3: Invoke the method load_model()
# optional Step 4: Use the setter method set_model_id(name: str) to give model a name

loading_predictor = BasicMultStepUniVar(steps_past: int, steps_future: int)
loading_predictor.load_model(location: str)
loading_predictor.set_model_id(name: str)

2. Univariate-Multistep forecasting - Hybrid architectures

from imbrium.predictors.univarhybrid import *

predictor = HybridMultStepUniVar(sub_seq: int, steps_past: int, steps_future: int, data = pd.DataFrame(), scale: str = '')

# Choose between one of the architectures:

# predictor.create_cnnrnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnnlstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnngru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnnbirnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnnbilstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnnbigru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')

# Fit the predictor object
predictor.fit_model(epochs: int, show_progress: int = 1, validation_split: float = 0.20, batch_size: int = 10)

# Have a look at the model performance - MSE based, more evaluation forms might be added on architecture level in the future
predictor.show_performance()

# Make a prediction based on new unseen data
predictor.predict(data: array)

# Safe your model:
predictor.save_model()

# Load a model:
# Step 1: initialize a new predictor object with same characteristics as model to load
# Step 2: Do not pass in any data
# Step 3: Invoke the method load_model()
# optional Step 4: Use the setter method set_model_id(name: str) to give model a name

loading_predictor =  HybridMultStepUniVar(sub_seq: int, steps_past: int, steps_future: int)
loading_predictor.load_model(location: str)
loading_predictor.set_model_id(name: str)

Multivariate Models:

1. Multivariate-Multistep forecasting - Standard architectures

from imbrium.predictors.multivarstandard import *

# please make sure that the target feature is the first variable in the feature list
predictor = BasicMultStepMultVar(steps_past: int, steps_future: int, data = pd.DataFrame(), features = [], scale: str = '')

# Choose between one of the architectures:

# predictor.create_mlp(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_rnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_lstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_gru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_birnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_bilstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_bigru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')

# Fit the predictor object
predictor.fit_model(epochs: int, show_progress: int = 1, validation_split: float = 0.20, batch_size: int = 10)

# Have a look at the model performance - MSE based, more evaluation forms might be added on architecture level in the future
predictor.show_performance()

# Make a prediction based on new unseen data
predictor.predict(data: array)

# Safe your model:
predictor.save_model()

# Load a model:
# Step 1: initialize a new predictor object with same characteristics as model to load
# Step 2: Do not pass in any data
# Step 3: Invoke the method load_model()
# optional Step 4: Use the setter method set_model_id(name: str) to give model a name

loading_predictor = BasicMultStepMultVar(steps_past: int, steps_future: int)
loading_predictor.load_model(location: str)
loading_predictor.set_model_id(name: str)

2. Multivariate-Multistep forecasting - Hybrid architectures

from imbrium.predictors.multivarhybrid import *

# please make sure that the target feature is the first variable in the feature list
predictor = HybridMultStepMultVar(sub_seq: int, steps_past: int, steps_future: int, data = pd.DataFrame(), features:list = [], scale: str = '')

# Choose between one of the architectures:

# predictor.create_cnnrnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnnlstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnngru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnnbirnn(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnnbilstm(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')
# predictor.create_cnnbigru(optimizer: str = 'adam', loss: str = 'mean_squared_error', metrics: str = 'mean_squared_error')

# Fit the predictor object
predictor.fit_model(epochs: int, show_progress: int = 1, validation_split: float = 0.20, batch_size: int = 10)

# Have a look at the model performance - MSE based, more evaluation forms might be added on architecture level in the future
predictor.show_performance()

# Make a prediction based on new unseen data
predictor.predict(data: array)

# Safe your model:
predictor.save_model()

# Load a model:
# Step 1: initialize a new predictor object with same characteristics as model to load
# Step 2: Do not pass in any data
# Step 3: Invoke the method load_model()
# optional Step 4: Use the setter method set_model_id(name: str) to give model a name

loading_predictor =  HybridMultStepMultVar(sub_seq: int, steps_past: int, steps_future: int)
loading_predictor.load_model(location: str)
loading_predictor.set_model_id(name: str)

References

Brwonlee, J., 2016. Display deep learning model training history in keras [Online]. Available from: https://machinelearningmastery.com/display-deep- learning-model-training-history-in-keras/.

Brwonlee, J., 2018a. How to develop convolutional neural network models for time series forecasting [Online]. Available from: https://machinelearningmastery.com/how-to-develop-convolutional- neural-network-models-for-time-series-forecasting/.

Brwonlee, J., 2018b. How to develop lstm models for time series forecasting [Online]. Available from: https://machinelearningmastery.com/how-to-develop- lstm-models-for-time-series-forecasting/.

Brwonlee, J., 2018c. How to develop multilayer perceptron models for time series forecasting [Online]. Available from: https://machinelearningmastery.com/how-to-develop-multilayer- perceptron-models-for-time-series-forecasting/.