https://github.com/wikke/TimeSeriesPrediction

Time Series Prediction, Stateful LSTM; 时间序列预测，洗发水销量/股票走势预测，有状态循环神经网络
https://github.com/wikke/TimeSeriesPrediction

lstm-neural-networks timeseries timeseries-sequence

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Time Series Prediction, Stateful LSTM; 时间序列预测，洗发水销量/股票走势预测，有状态循环神经网络

• Host: GitHub
• URL: https://github.com/wikke/TimeSeriesPrediction
• Owner: wikke
• Created: 2017-08-29T07:21:40.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2017-08-29T09:08:56.000Z (almost 7 years ago)
• Last Synced: 2024-01-18T16:14:15.300Z (5 months ago)
• Topics: lstm-neural-networks, timeseries, timeseries-sequence
• Language: Jupyter Notebook
• Homepage:
• Size: 805 KB
• Stars: 47
• Watchers: 3
• Forks: 19
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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• awesome-ml-summary - TimeSeriesPrediction

README

        
# Time Series Predictions

> Play with time

## 1. Shampoo Sales Prediction

> `ShampooSales.ipynb`

sales goes like this, need to predict according to history.



A wonderful tutorial to convert time series prediction to supervised problem: [Time Series Forecasting as Supervised Learning](https://machinelearningmastery.com/time-series-forecasting-supervised-learning/)

### Result

best fit before overfitting:



### Stateful LSTM

Core code

```

model = Sequential()

model.add(LSTM(4, batch_input_shape=(BATCH_SIZE, X.shape[1], X.shape[2]), stateful=True))

model.add(Dropout(0.5))

model.add(Dense(1, activation='linear'))

model.compile(loss='mse', optimizer='adadelta')

# way 1

for i in range(EPOCHS):

    model.fit(X, y, epochs=1, shuffle=False, batch_size=BATCH_SIZE)

    model.reset_states()

# way 2

class StatusResetCallback(Callback):

    def on_batch_begin(self, batch, logs={}):

        self.model.reset_states()

model.fit(X, y, epochs=EPOCHS, batch_size=BATCH_SIZE,

		shuffle=False, callbacks=[StatusResetCallback()])

```

## 2. Stateful LSTM in Keras

> `StatefulLSTM.ipynb`

Learning from [Stateful LSTM in Keras](http://philipperemy.github.io/keras-stateful-lstm/) by Philippe Remy, which is a wonderful and simple tutorial. The composed dataset is simple and clean:

```

     X           y

1 0 0 ... 0      1

0 0 0 ... 0      0

0 0 0 ... 0      0

1 0 0 ... 0      1

1 0 0 ... 0      1

...

```

Obviously, if the first of X seq is 1, y = 1, else 0. We will see if the 1 status will pass along to predict the result.

### Stateless LSTM Can't Converge

```

model = Sequential()

model.add(LSTM(LSTM_UNITS, input_shape=X_train.shape[1:], return_sequences=False, stateful=False))

model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

```

### Stateful LSTM

it works. Talk is cheap, see the code.