Time-series-prediction

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Time-series-prediction

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tfts: Time Series Deep Learning Models in TensorFlow

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**[Documentation](https://time-series-prediction.readthedocs.io)** | **[Tutorials](https://time-series-prediction.readthedocs.io/en/latest/tutorials.html)** | **[Release Notes](./CHANGELOG.md)** | **[中文](https://github.com/LongxingTan/Time-series-prediction/blob/master/README_CN.md)**

**TFTS** (TensorFlow Time Series) is an easy-to-use time series package, supporting the classical and latest deep learning methods in TensorFlow or Keras.
- Support sota models for time series tasks (prediction, classification, anomaly detection)
- Provide advanced deep learning models for industry, research and competition
- Documentation lives at [time-series-prediction.readthedocs.io](https://time-series-prediction.readthedocs.io)

## Tutorial

**Installation**

- python >= 3.7
- tensorflow >= 2.4

```shell
pip install tfts
```

**Quick start**

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LHdbrXmQGBSQuNTsbbM5-lAk5WENWF-Q?usp=sharing)
[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://www.kaggle.com/code/tanlongxing/tensorflow-time-series-starter-tfts/notebook)

```python
import matplotlib.pyplot as plt
import tensorflow as tf
import tfts
from tfts import AutoModel, AutoConfig, KerasTrainer

train_length = 24
predict_sequence_length = 8
(x_train, y_train), (x_valid, y_valid) = tfts.get_data("sine", train_length, predict_sequence_length, test_size=0.2)

model_name_or_path = 'seq2seq'  # 'wavenet', 'transformer', 'rnn', 'tcn', 'bert', 'dlinear', 'nbeats', 'informer', 'autoformer'
config = AutoConfig.for_model(model_name_or_path)
model = AutoModel.from_config(config, predict_sequence_length=predict_sequence_length)
trainer = KerasTrainer(model, optimizer=tf.keras.optimizers.Adam(0.0007))
trainer.train((x_train, y_train), (x_valid, y_valid), epochs=30)

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`
- option3 `tf.keras.utils.Sequence`

Encoder only model inputs

```python
import numpy as np
from tfts import AutoConfig, AutoModel, KerasTrainer

train_length = 24
predict_sequence_length = 8
n_feature = 2

x_train = np.random.rand(1, train_length, n_feature)  # inputs: (batch, train_length, feature)
y_train = np.random.rand(1, predict_sequence_length, 1)  # target: (batch, predict_sequence_length, 1)
x_valid = np.random.rand(1, train_length, n_feature)
y_valid = np.random.rand(1, predict_sequence_length, 1)

config = AutoConfig.for_model('rnn')
model = AutoModel.from_config(config, predict_sequence_length=predict_sequence_length)
trainer = KerasTrainer(model)
trainer.train(train_dataset=(x_train, y_train), valid_dataset=(x_valid, y_valid), epochs=1)
```

Encoder-decoder model inputs

```python
# option1: np.ndarray
import numpy as np
from tfts import AutoConfig, AutoModel, KerasTrainer

train_length = 24
predict_sequence_length = 8
n_encoder_feature = 2
n_decoder_feature = 3

x_train = (
    np.random.rand(1, train_length, 1),  # inputs: (batch, train_length, 1)
    np.random.rand(1, train_length, n_encoder_feature),  # encoder_feature: (batch, train_length, encoder_features)
    np.random.rand(1, predict_sequence_length, n_decoder_feature),  # decoder_feature: (batch, predict_sequence_length, decoder_features)
)
y_train = np.random.rand(1, predict_sequence_length, 1)  # target: (batch, predict_sequence_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_sequence_length, n_decoder_feature),
)
y_valid = np.random.rand(1, predict_sequence_length, 1)

config = AutoConfig.for_model("seq2seq")
model = AutoModel.from_config(config, predict_sequence_length=predict_sequence_length)
trainer = KerasTrainer(model)
trainer.train((x_train, y_train), (x_valid, y_valid), epochs=1)
```

```python
# option2: tf.data.Dataset
import numpy as np
import tensorflow as tf
from tfts import AutoConfig, AutoModel, KerasTrainer

class FakeReader(object):
    def __init__(self, predict_sequence_length):
        train_length = 24
        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_sequence_length, n_decoder_feature)
        self.target = np.random.rand(15, predict_sequence_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_sequence_length = 10
train_reader = FakeReader(predict_sequence_length=predict_sequence_length)
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_sequence_length=predict_sequence_length)
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)

config = AutoConfig.for_model("seq2seq")
model = AutoModel.from_config(config, predict_sequence_length=predict_sequence_length)
trainer = KerasTrainer(model)
trainer.train(train_dataset=train_loader, valid_dataset=valid_loader, epochs=1)
```

**Prepare custom model config**

```python
from tfts import AutoModel, AutoConfig

config = AutoConfig.for_model('rnn')
print(config)
config.rnn_hidden_size = 128

model = AutoModel.from_config(config, predict_sequence_length=7)
```

**Build your own model**

<details><summary> Full list of tfts AutoModel supported </summary>

- rnn
- tcn
- bert
- nbeats
- dlinear
- seq2seq
- wavenet
- transformer
- informer
- autoformer

</details>

You could build the custom model based on tfts, like
- add custom-defined embeddings for categorical variables
- add custom-defined head layers for classification or anomaly task

```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tfts import AutoModel, AutoConfig

train_length = 24
num_train_features = 15
predict_sequence_length = 8

def build_model():
    inputs = Input([train_length, num_train_features])
    config = AutoConfig.for_model("seq2seq")
    backbone = AutoModel.from_config(config, predict_sequence_length=predict_sequence_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
```

## Examples

- [TFTS-Bert](https://github.com/LongxingTan/KDDCup2022-Baidu) wins the **3rd place** in KDD Cup 2022-wind power forecasting
- [TFTS-Seq2seq](https://github.com/LongxingTan/Data-competitions/tree/master/tianchi-enso-prediction) wins the **4th place** in Tianchi-ENSO index prediction 2021
- [More examples ...](./examples)

<!-- ### Performance

[Time series prediction](./examples/run_prediction_simple.py) performance is evaluated by tfts implementation, not official

| Performance | [web traffic<sup>mape</sup>]() | [grocery sales<sup>wrmse</sup>](https://www.kaggle.com/competitions/favorita-grocery-sales-forecasting/data) | [m5 sales<sup>val</sup>]() | [ventilator<sup>val</sup>]() |
| :-- | :-: | :-: | :-: | :-: |
| [RNN]() | 672 | 47.7% |52.6% | 61.4% |
| [DeepAR]() | 672 | 47.7% |52.6% | 61.4% |
| [Seq2seq]() | 672 | 47.7% |52.6% | 61.4% |
| [TCN]() | 672 | 47.7% |52.6% | 61.4% |
| [WaveNet]() | 672 | 47.7% |52.6% | 61.4% |
| [Bert]() | 672 | 47.7% |52.6% | 61.4% |
| [Transformer]() | 672 | 47.7% |52.6% | 61.4% |
| [Temporal-fusion-transformer]() | 672 | 47.7% |52.6% | 61.4% |
| [Informer]() | 672 | 47.7% |52.6% | 61.4% |
| [AutoFormer]() | 672 | 47.7% |52.6% | 61.4% |
| [N-beats]() | 672 | 47.7% |52.6% | 61.4% |
| [U-Net]() | 672 | 47.7% |52.6% | 61.4% |

### More demos
- [More complex prediction task](./notebooks)
- [Time series classification](./examples/run_classification.py)
- [Anomaly detection](./examples/run_anomaly.py)
- [Uncertainty prediction](examples/run_uncertainty.py)
- [Parameters tuning by optuna](examples/run_optuna_tune.py)
- [Serving by tf-serving](./examples) -->

## Citation

If you find tfts project useful in your research, please consider cite:

```
@misc{tfts2020,
  author = {Longxing Tan},
  title = {TFTS: Time series prediction},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/longxingtan/time-series-prediction}},
}
```

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