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This library uses nbeats-pytorch as base and simplifies the task of univariate time series forecasting using N-BEATS by providing a interface similar to scikit-learn and keras.

Project description

nbeats_forecast

Neural Beats implementation library

nbeats_forecast is an end to end library for univariate time series forecasting using N-BEATS (https://arxiv.org/pdf/1905.10437v3.pdf - Published as conference paper in ICLR). This library uses nbeats-pytorch (https://github.com/philipperemy/n-beats) as base and simplifies the task of forecasting using N-BEATS by providing a interface similar to scikit-learn and keras.

Required: Python >=3.6

Installation

$ pip install nbeats_forecast

Import

from nbeats_forecast import NBeats

Input

numpy array of size nx1

Output

Forecasted values as numpy array of size mx1

Mandatory Parameters for the model:

  • data
  • period_to_forecast

Basic model with only mandatory parameters can be used to get forecasted values as shown below:

import pandas as pd
from nbeats_forecast import NBeats

data = pd.read_csv('data.csv')   
data = data.values        #univariate time series data of shape nx1 (numpy array)

model = NBeats(data=data, period_to_forecast=12)
model.fit()
forecast = model.predict()

Other optional parameters for the object of the model (as described in the paper) can be tweaked for better performance. If these parameters are not passed, default values as mentioned in the table below are considered.

Optional parameters

Parameter Type Default Value Description
backcast_length integer 3* period_to_forecast Explained in the paper
path string ' ' path to save intermediate training checkpoint
checkpoint_file_name string 'nbeats-training-checkpoint.th' name for checkpoint file ending in format .th
mode string 'cpu' Any of the torch.device modes
batch_size integer len(data)/15 size of batch
thetas_dims list of integers [7, 8] Explained in the paper
nb_blocks_per_stack integer 3 Explained in the paper
share_weights_in_stack boolean False Explained in the paper
train_percent float(below 1) 0.8 Percentage of data to be used for training
save_checkpoint boolean False save intermediate checkpoint files
hidden_layer_units integer 128 hissen layer units
stack list of integers [1,1] adding stacks in the model as per the paper passed in list as integer. Mapping is as follows -- 1: GENERIC_BLOCK, 2: TREND_BLOCK , 3: SEASONALITY_BLOCK

Methods

fit(epoch,optimiser,plot,verbose):

This method is used to train the model for number of gradient steps passed in the object of the model.

Parameter - epoch : integer

epoch is 100 * gradient steps. 25 epoch means 2500 weight updation steps. If optimizer is not passed, default value is 25.

Parameter - optimiser

optimizer from torch.optim can be passed as a parameter by including model.parameters as the variable.

Example:

model.fit(epoch=5,optimiser=torch.optim.AdamW(model.parameters, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.01, amsgrad=False))

If optimizer is not passed, default optimizer used is Adam.

Parameter- plot : boolean

Default value - False

If True, plots during training are shown.

Parameter- verbose : boolean

Default value - True

If True, training details are printed.

predict(predict_data):

Parameter - predict_data (optional) : numpy array of size backcast_length x 1

If predict_data is not passed, forecasted values returned will be continued from the last value of data passed in fit() during training.

UPDATE:

Added functionality:
Predicting for other data

Passing predict_data:

To get predictions for the some other data from trained model, pass the predict_data as a numpy array of shape backcast_length x 1 (default value for backcast length is 3* preiod_to_forecast).

Example:

You have trained a model for temperature forecast with hourly temperature data, period_to_forecast=4 and backcast_length=16 for Timestamp "13-12-2019 08:00:00" to "31-12-2019 08:00:00".

Now you want to predict temperature from Timestamp "14-01-2020 17:00:00" using the trained model. You need to pass past data as predict_data with window equal to backcast_length(16 here). Here you need to pass values from "14-01-2020 01:00:00" to "14-01-2020 16:00:00"(16 values here) as numpy array of shape backcast_length x 1 (Here 16 x 1).

Returns forecasted values.

save(file):

Saves the current step model after training. File needs to be passed a string. Format of the model to be saved is .th

Example: model.save('model.th')

load(file,optimizer):

Parameter - file

Load the saved model with format .th

Parameter - optimiser

optimizer from torch.optim can be passed as a parameter by including model.parameters as the variable.

Example: model.load('model.th')

EXAMPLES BELOW

  1. Model with a different optimiser and different stack is shown here.
  2. Predicting for other data
  3. Continue training via the saved file or retrain with new data(load saved model and retrain)
  4. Load saved model and predict with data

Example 1

Model with a different optimiser and different stack is shown here.

Here 2: TREND_BLOCK and 3: SEASONALITY_BLOCK stacks are used.

import pandas as pd
from nbeats_forecast import NBeats
from torch import optim

data = pd.read_csv('data.csv')   
data = data.values #univariate time series data of shape nx1(numpy array)

model=NBeats(data=data,period_to_forecast=12,stack=[2,3],nb_blocks_per_stack=3,thetas_dims=[2,8])

model.fit(epoch=5,optimiser=optim.AdamW(model.parameters, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.01, amsgrad=False))

forecast=model.predict()

Example 2

Predicting for other data with backcast_length=12.

import pandas as pd
from nbeats_forecast import NBeats
from torch import optim

data = pd.read_csv('new_data.csv')   
data = data.values #univariate time series data of shape nx1(numpy array)

model=NBeats(data=data,period_to_forecast=12,stack=[2,3],nb_blocks_per_stack=3,thetas_dims=[2,8]) 

model.fit()

list1=[36.7,38.5,39.4,36.75,38,39,38,37.45,38,39,39.5,40]
pred=np.asarray(list1)

forecast=model.predict(predict_data=pred)

Example 3

Continue training via the saved file or retrain with new data

import pandas as pd
from nbeats_forecast import NBeats
from torch import optim

new_datdata = pd.read_csv('new_data.csv')   
data = data.values #univariate time series data of shape nx1(numpy array)

model=NBeats(data=data,period_to_forecast=12,stack=[2,3],nb_blocks_per_stack=3,thetas_dims=[2,8]) 
# use same model definition as saved model
model.load('nbeats.th',optimiser=optim.AdamW(model.parameters, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0.01, amsgrad=False))
)
model.fit()


forecast=model.predict()

Example 4

Load saved model and predict with data

from nbeats_forecast import NBeats
import numpy as np

# use same model definition as saved model
model=NBeats(period_to_forecast=4,stack=[2,3],nb_blocks_per_stack=3,thetas_dims=[2,8]) 

model.load(file='nbeats.th')

list1=[36.7,38.5,39.4,36.75,38,39,38,37.45,38,39,39.5,40]
pred=np.asarray(list1)
forecast=model.predict(pred)

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