Keras Timeseries Prediction
Time series prediction with Sequential Model and LSTM units
Install / Use
npx skills add gcarq/keras-timeseries-predictionInstalls into whichever agent you are using.
README
Time series predictions with Keras
Requirements
- Theano
- Keras
- matplotlib
- pandas
- scikit-learn
- tqdm
- numpy
Usage
git clone https://github.com/gcarq/keras-timeseries-prediction.git
cd keras-timeseries-prediction/
pip install -r requirements.txt
python main.py
Dataset
The dataset is international-airline-passengers.csv which contains 144 data points ranging from Jan 1949 to Dec 1960.
Each data point represents monthly passengers in thousands.
Model
model = Sequential()
model.add(LSTM(64,
activation='relu',
batch_input_shape=(batch_size, look_back, 1),
stateful=True,
return_sequences=False))
model.add(Dense(1, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
Results
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