Time series prediction with fastai, pytorch and prophet
Project description
ProFeTorch
FB Prophet + Fastai + pyTorch.
This is an alternative implementation of prophet which uses quantile regression instead of MCMC sampling. It provides the following benefits over prophet:
 GPU usage.
 Strict(er) upper and lower bounds.
 Can add any other set of features to the time series.
The time series is implemented as follows:
\begin{aligned} y &= b(T(t) + S(t) + F(x)l,u) \ T(t) &= mt + a \ S(t) &= \sum_{n=1}^N\left(a_n \cos\left(\frac{2\pi nt}{P}\right) + b_n \sin\left(\frac{2\pi nt}{P}\right)\right) \ F(x) &= w^T x\ b(yl,u) &= \begin{cases} l \quad \text{if } y < l \ y \quad \text{if } l < y < u \ u \quad \text{if } y > u \end{cases} \end{aligned}
where $T(t)$ is the trend line, $S(t)$ are the seasonal components composed of a fourier sum, $F(x)$ is a linear function which weights features that is not related to time.
The task is therefore to find the parameters $a, m, \cup_n a_n, \cup_n b_n, w$ that minimises a loss function $l(\hat{y}, y)$. The default is set to minimise $l1$ loss $\frac{1}{N}\sum_{i=1}^N y_i  \hat{y_i}$ so that the reliance on outliers is minimised. By default we also calculate the 5th and 95th quantile by minimising the tilted loss function. The quantile functions are calculated as: \begin{align} y_5 &= b(\hat{y}  (m_5 t + a_5)l,u) \ y_{95} &= b(\hat{y} + (m_{95} t + a_{95})l,u) \end{align}
Install
pip install profetorch
ProFeTorch Training
model_params = {'y_n':10, 'm_n':7, 'l':0, 'h': train_df['y'].max() * 2} model = Model(train_df, model_args=model_params, epochs=30, alpha=1e2, beta=0) model.fit(train_df)
/opt/miniconda3/lib/python3.7/sitepackages/pandas/core/frame.py:4117: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandasdocs/stable/user_guide/indexing.html#returningaviewversusacopy
errors=errors,
Epoch 30/30 Training Loss: 0.3687, Validation Loss: 0.6105
y_pred = model.predict(df) plt.figure(figsize=(12,5)) plt.scatter(df['ds'], df['y'], label='Data') plt.plot(train_df['ds'], y_pred[:train_len,1], c='r', label='Train Set') plt.fill_between(train_df['ds'], y_pred[:train_len,0], y_pred[:train_len,2], alpha=0.5) plt.plot(test_df['ds'], y_pred[train_len:,1], c='g', label='Test Set') plt.fill_between(test_df['ds'], y_pred[train_len:,0], y_pred[train_len:,2], alpha=0.5) plt.show()
Obviously more works needs to be done as seen in the graph above. However, note that:
 The seasonal component is captured.
 The quantiles are asymmetric, which cannot happen in the fbprophet case.
 I will fix these short comings if there is enough interest.
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