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TFTS (TensorFlow Time Series) is an easy-to-use python package for time series, supporting the classical and SOTA deep learning methods in TensorFlow or Keras.
- Flexible and powerful design for time series task
- Advanced deep learning models for industry, research and competition
- Documentation lives at time-series-prediction.readthedocs.io
Installation
- python >= 3.7
- tensorflow >= 2.3
$ pip install tftsBasic usage
import matplotlib.pyplot as plt
import tfts
from tfts import AutoModel, AutoConfig, KerasTrainer
train_length = 24
predict_length = 8
(x_train, y_train), (x_valid, y_valid) = tfts.get_data("sine", train_length, predict_length, test_size=0.2)
model = AutoModel("seq2seq", predict_length=predict_length)
trainer = KerasTrainer(model)
trainer.train((x_train, y_train), (x_valid, y_valid), n_epochs=3)
pred = trainer.predict(x_valid)
trainer.plot(history=x_valid, true=y_valid, pred=pred)
plt.show()Prepare your own data
You could train your own data by preparing 3D data as inputs, for both inputs and targets
- option1
np.ndarray - option2
tf.data.Dataset
Encoder only model inputs
train_length = 49
predict_length = 10
n_feature = 2
x_train = np.random.rand(1, train_length, n_feature)
y_train = np.random.rand(1, predict_length, 1)
x_valid = np.random.rand(1, train_length, n_feature)
y_valid = np.random.rand(1, predict_length, 1)
model = AutoModel("rnn", predict_length=predict_length)
trainer = KerasTrainer(model)
trainer.train(train_dataset=(x_train, y_train), valid_dataset=(x_valid, y_valid), n_epochs=1)Encoder-decoder model inputs
# option1: np.ndarray
train_length = 49
predict_length = 10
n_encoder_feature = 2
n_decoder_feature = 3
x_train = (
np.random.rand(1, train_length, 1),
np.random.rand(1, train_length, n_encoder_feature),
np.random.rand(1, predict_length, n_decoder_feature),
)
y_train = np.random.rand(1, predict_length, 1)
x_valid = (
np.random.rand(1, train_length, 1),
np.random.rand(1, train_length, n_encoder_feature),
np.random.rand(1, predict_length, n_decoder_feature),
)
y_valid = np.random.rand(1, predict_length, 1)
model = AutoModel("seq2seq", predict_length=predict_length)
trainer = KerasTrainer(model)
trainer.train((x_train, y_train), (x_valid, y_valid), n_epochs=1)# option2: tf.data.Dataset
class FakeReader(object):
def __init__(self, predict_length=10):
train_length = 49
n_encoder_feature = 2
n_decoder_feature = 3
self.x = np.random.rand(15, train_length, 1)
self.encoder_feature = np.random.rand(15, train_length, n_encoder_feature)
self.decoder_feature = np.random.rand(15, predict_length, n_decoder_feature)
self.target = np.random.rand(15, predict_length, 1)
def __len__(self):
return len(self.x)
def __getitem__(self, idx):
return {
"x": self.x[idx],
"encoder_feature": self.encoder_feature[idx],
"decoder_feature": self.decoder_feature[idx],
}, self.target[idx]
def iter(self):
for i in range(len(self.x)):
yield self[i]
predict_length = 10
train_reader = FakeReader(predict_length=10)
train_loader = tf.data.Dataset.from_generator(
train_reader.iter,
({"x": tf.float32, "encoder_feature": tf.float32, "decoder_feature": tf.float32}, tf.float32),
)
train_loader = train_loader.batch(batch_size=1)
valid_reader = FakeReader(predict_length=10)
valid_loader = tf.data.Dataset.from_generator(
valid_reader.iter,
({"x": tf.float32, "encoder_feature": tf.float32, "decoder_feature": tf.float32}, tf.float32),
)
valid_loader = valid_loader.batch(batch_size=1)
model = AutoModel("seq2seq", predict_length=predict_length)
trainer = KerasTrainer(model)
trainer.train(train_dataset=train_loader, valid_dataset=valid_loader, n_epochs=1)Build your own model
You could build the custom model based on tfts backbone
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tfts import AutoModel
def build_model():
train_length = 24
train_features = 15
predict_length = 16
inputs = Input([train_length, train_features])
backbone = AutoModel("seq2seq", predict_length=predict_length)
outputs = backbone(inputs)
outputs = Dense(1, activation="sigmoid")(outputs)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss="mse", optimizer="rmsprop")
return model- TFTS-Bert wins the 3rd place in KDD Cup 2022 Baidu-wind power forecasting
- TFTS-Seq2seq wins the 4th place in Alibaba Tianchi-ENSO prediction 2021
if you prefer other DL frameworks, try pytorch-forecasting, gluonts, paddlets
If you find tfts project useful in your research, please consider cite:
@misc{tfts2020,
author = {Longxing Tan},
title = {Time series prediction},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/longxingtan/time-series-prediction}},
}
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