def test_ensemble_models_denoising_multi_input(self):
# for every model, test whether it correctly denoises ts_sum_2 using ts_random_multi and ts_sum_2 as inputs
# WARNING: this test isn't numerically stable, changing self.RANDOM_SEED can lead to exploding coefficients
horizon = 10
_, _, ts_sum2, ts_cov2 = self.denoising_input()
torch.manual_seed(self.RANDOM_SEED)
ensemble_models = [
RNNModel(
input_chunk_length=20,
output_chunk_length=horizon,
n_epochs=1,
random_state=self.RANDOM_SEED,
),
BlockRNNModel(
input_chunk_length=20,
output_chunk_length=horizon,
n_epochs=1,
random_state=self.RANDOM_SEED,
),
RegressionModel(lags_past_covariates=[-1]),
RegressionModel(lags_past_covariates=[-1]),
]
ensemble = RegressionEnsembleModel(ensemble_models, horizon)
self.helper_test_models_accuracy(ensemble, horizon, ts_sum2, ts_cov2,
3)
def lstm():
for company in lstCompanies:
df = pd.DataFrame(list(db[company].find({})))
df = df.drop('_id', axis=1)
df['Open'] = df['Open'].astype('float')
df['Close'] = df['Close'].astype('float')
series = TimeSeries.from_dataframe(
df, 'Date', ['Close'], freq='B',
fill_missing_dates=True) # 'B' = Business day
series = auto_fillna(series)
model = RNNModel(
model=
'LSTM', # Either a string specifying the RNN module type (“RNN”, “LSTM” or “GRU”)
output_length=
1, # Number of time steps to be output by the forecasting module
hidden_size=
25, # Size for feature maps for each hidden RNN layer (hn)
n_rnn_layers=1, # Number of layers in the RNN module
input_length=
12, # The dimensionality of the TimeSeries instances that will be fed to the fit function
batch_size=
16, # The batch size is a hyperparameter that defines the number of samples to work through before updating the internal model parameters
n_epochs=
200, # The number of epochs is a hyperparameter that defines the number times that the learning algorithm will work through the entire training dataset
optimizer_kwargs={'lr': 1e-3},
model_name='{}_RNN'.format(company))
model.fit(series)
lstmPred = model.predict(1).values()[0][0]
db.prediction.insert_one({
"Date": datetime.datetime.today(),
"Company": company,
"Prediction": round(float(lstmPred), 2)
})
def test_likelihoods_and_resulting_mean_forecasts(self):
def _get_avgs(series):
return np.mean(series.all_values()[:, 0, :]), np.mean(
series.all_values()[:, 1, :])
for lkl, series, diff1, diff2 in self.lkl_series:
model = RNNModel(input_chunk_length=5, likelihood=lkl)
model.fit(series, epochs=50)
pred = model.predict(n=50, num_samples=50)
avgs_orig, avgs_pred = _get_avgs(series), _get_avgs(pred)
self.assertLess(
abs(avgs_orig[0] - avgs_pred[0]),
diff1,
"The difference between the mean forecast and the mean series is larger "
"than expected on component 0 for distribution {}".format(
lkl),
)
self.assertLess(
abs(avgs_orig[1] - avgs_pred[1]),
diff2,
"The difference between the mean forecast and the mean series is larger "
"than expected on component 1 for distribution {}".format(
lkl),
)
def test_call_predict_global_models_multivariate_input_no_covariates(self):
naive_ensemble = NaiveEnsembleModel([
RNNModel(12, n_epochs=1),
TCNModel(10, 2, n_epochs=1),
NBEATSModel(10, 2, n_epochs=1),
])
naive_ensemble.fit(self.seq1)
naive_ensemble.predict(n=5, series=self.seq1)
def test_call_predict_global_models_univariate_input_no_covariates(self):
naive_ensemble = NaiveEnsembleModel([
RNNModel(12, n_epochs=1),
TCNModel(10, 2, n_epochs=1),
NBEATSModel(10, 2, n_epochs=1),
])
with self.assertRaises(Exception):
naive_ensemble.predict(5)
naive_ensemble.fit(self.series1)
naive_ensemble.predict(5)
def test_max_samples_per_ts(self):
"""
Checking that we can fit TorchForecastingModels with max_samples_per_ts, without crash
"""
ts = linear_timeseries(start_value=0, end_value=1, length=50)
model = RNNModel(input_chunk_length=20,
output_chunk_length=2,
n_epochs=2,
batch_size=32)
model.fit(ts, max_samples_per_ts=5)
def test_call_predict_global_models_multivariate_input_with_covariates(
self):
naive_ensemble = NaiveEnsembleModel([
RNNModel(12, n_epochs=1),
TCNModel(10, 2, n_epochs=1),
NBEATSModel(10, 2, n_epochs=1),
])
naive_ensemble.fit(self.seq1, self.cov1)
predict_series = [s[:12] for s in self.seq1]
predict_covariates = [c[:14] for c in self.cov1]
naive_ensemble.predict(n=2,
series=predict_series,
past_covariates=predict_covariates)
def get_global_models(self, output_chunk_length=5):
return [
RNNModel(
input_chunk_length=20,
output_chunk_length=output_chunk_length,
n_epochs=1,
random_state=42,
),
BlockRNNModel(
input_chunk_length=20,
output_chunk_length=output_chunk_length,
n_epochs=1,
random_state=42,
),
]
def test_sample_smaller_than_batch_size(self):
"""
Checking that the TorchForecastingModels do not crash even if the number of available samples for training
is strictly lower than the selected batch_size
"""
# TS with 50 timestamps. TorchForecastingModels will use the SequentialDataset for producing training
# samples, which means we will have 50 - 22 - 2 + 1 = 27 samples, which is < 32 (batch_size). The model
# should still train on those samples and not crash in any way
ts = linear_timeseries(start_value=0, end_value=1, length=50)
model = RNNModel(input_chunk_length=20,
output_chunk_length=2,
n_epochs=2,
batch_size=32)
model.fit(ts)
def test_future_covariates(self):
# models with future covariates should produce better predictions over a long forecasting horizon
# than a model trained with no covariates
model = TCNModel(
input_chunk_length=50,
output_chunk_length=5,
n_epochs=20,
random_state=0,
)
model.fit(series=self.target_past)
long_pred_no_cov = model.predict(n=160)
model = TCNModel(
input_chunk_length=50,
output_chunk_length=5,
n_epochs=20,
random_state=0,
)
model.fit(series=self.target_past,
past_covariates=self.covariates_past)
long_pred_with_cov = model.predict(n=160,
past_covariates=self.covariates)
self.assertTrue(
mape(self.target_future, long_pred_no_cov) > mape(
self.target_future, long_pred_with_cov),
"Models with future covariates should produce better predictions.",
)
# block models can predict up to self.output_chunk_length points beyond the last future covariate...
model.predict(n=165, past_covariates=self.covariates)
# ... not more
with self.assertRaises(ValueError):
model.predict(n=166, series=self.ts_pass_train)
# recurrent models can only predict data points for time steps where future covariates are available
model = RNNModel(12, n_epochs=1)
model.fit(series=self.target_past,
future_covariates=self.covariates_past)
model.predict(n=160, future_covariates=self.covariates)
with self.assertRaises(ValueError):
model.predict(n=161, future_covariates=self.covariates)
def make_lstm_prediction():
for ticker in lst_tickers_of_interest:
df_ticker = pd.DataFrame(
list(col_price_history.find({'Ticker':
ticker})))[["DailyChangePct",
"Date"]].set_index('Date')
df_ticker.index = pd.to_datetime(df_ticker.index)
df_ticker = df_ticker.reindex(index=df_ticker.index[::-1])
series = TimeSeries.from_dataframe(df_ticker,
time_col=None,
value_cols='DailyChangePct',
freq='B',
fill_missing_dates=True)
series = auto_fillna(series)
SEQ_LENGTH = 6
HIDDEN_SIZE = 5
OUTPUT_LEN = 1
NUM_LAYERS = 1
model = RNNModel(model='LSTM',
output_length=OUTPUT_LEN,
hidden_size=HIDDEN_SIZE,
n_rnn_layers=NUM_LAYERS,
input_length=SEQ_LENGTH,
batch_size=16,
n_epochs=10,
optimizer_kwargs={'lr': 1e-3},
model_name=f'{ticker}_RNN',
log_tensorboard=False)
model.fit(series)
lstm_prediction = model.predict(1).values()[0][0]
lstm_prediction_history.insert_one({
"Date":
datetime.datetime.today(),
"Ticker":
ticker,
"LSTM_prediction":
float(lstm_prediction)
})
def test_stochastic_inputs(self):
model = RNNModel(input_chunk_length=5)
model.fit(self.constant_ts, epochs=2)
# build a stochastic series
target_vals = self.constant_ts.values()
stochastic_vals = np.random.normal(loc=target_vals,
scale=1.0,
size=(len(self.constant_ts),
100))
stochastic_vals = np.expand_dims(stochastic_vals, axis=1)
stochastic_series = TimeSeries.from_times_and_values(
self.constant_ts.time_index, stochastic_vals)
# A deterministic model forecasting a stochastic series
# should return stochastic samples
preds = [
model.predict(series=stochastic_series, n=10) for _ in range(2)
]
# random samples should differ
self.assertFalse(
np.alltrue(preds[0].values() == preds[1].values()))
filler = MissingValuesFiller()
for model_type in model_types:
print(
f'\n##############################\nCurrent Model Type: {model_type}\n##############################\n',
file=sys.stderr)
# Create sub folder for each model type
if not os.path.isdir(
f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}'):
os.mkdir(f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}')
# Create model per model type
model = RNNModel(
model=model_type,
input_chunk_length=input_length,
output_chunk_length=output_length,
batch_size=input_length
) # batch_size must be <= input_length (bug fixed in Darts version 0.9.0)
for parameter in parameters:
print(
f'\n##############################\nCurrent Parameter: {parameter.upper()}\n'
f'##############################\n',
file=sys.stderr)
start_time = time.time()
# Create sub folder for each parameter
if not os.path.isdir(
f'./data/{approach}/{n_chunks}_chunks/{style}/{model_type}/{parameter}'
):
train, val = df_series.split_before(pd.Timestamp("2021-03-01 00:00:00+00:00"))
# Normalize the time series (note: we avoid fitting the transformer on the validation set)
transformer = Scaler()
train_transformed = transformer.fit_transform(train)
val_transformed = transformer.transform(val)
series_transformed = transformer.transform(df_series)
# Define the LSTM Model parameters
my_model = RNNModel(
model='LSTM',
input_chunk_length=24,
output_chunk_length=1,
hidden_size=25,
n_rnn_layers=1,
dropout=0.2,
batch_size=16,
n_epochs=20,
optimizer_kwargs={'lr': 1e-3},
model_name='Forecast_LSTM_next_hour',
log_tensorboard=True,
random_state=42
)
# Either train a new model or load best model from checkpoint
if train_new_model==True:
my_model.fit(train_transformed, val_series=val_transformed, verbose=True)
else:
my_model = RNNModel.load_from_checkpoint(model_name='Forecast_LSTM_next_hour', best=True)
# Evaluate Predictions
def eval_model(model):
def test_input_models_mixed(self):
with self.assertRaises(ValueError):
NaiveEnsembleModel([NaiveDrift(), Theta(), RNNModel(12)])
def test_input_models_global_models(self):
NaiveEnsembleModel([RNNModel(12), TCNModel(10, 2), NBEATSModel(10, 2)])
])
for model_type in configurations.model_types:
print(f'Current Model: {model_type}', file=sys.stderr)
for parameter in configurations.parameters:
print(f'Current Parameter: {parameter.upper()}', file=sys.stderr)
start_time = time.time()
for window_idx in range(configurations.n_windows):
print(f'Current Window: {window_idx}', file=sys.stderr)
# Create model
model = RNNModel(
model=model_type,
input_chunk_length=configurations.input_length,
output_chunk_length=configurations.output_length,
# batch_size must be <= input_length (bug fixed in Darts version 0.9.0)
batch_size=configurations.input_length)
# Read time-series input
train_series = read_pickle_file(
f'{script_path}/time_series/time_series_{parameter}_win{window_idx}_train_'
f'{endogenous_input}.pickle')
pred_series = read_pickle_file(
f'{script_path}/time_series/time_series_{parameter}_win{window_idx}_pred_'
f'{endogenous_input}.pickle')
print('Pre-train ...', file=sys.stderr)
# Pre-train with (e.g. 80%) of relevant MEDIAN series (steady training set)
print(len(val))
plot_acf(train, m=32, max_lag=240, alpha=.05)
#Normalize the time series (note: we avoid filtering the transformer on the validation set)
transformer = Scaler()
train_transformed = transformer.fit_transform(train)
val_transformed = transformer.transform(val)
series_transformed = transformer.transform(eq_series)
my_model = RNNModel(model='LSTM',
input_chunk_length=32,
output_chunk_length=1,
hidden_size=25,
n_rnn_layers=1,
dropout=0.4,
batch_size=16,
n_epochs=500,
optimizer_kwargs={'lr': 1e-3},
model_name='Eq_RNN',
log_tensorboard=True,
random_state=42)
my_model.fit(train_transformed, val_series=val_transformed, verbose=True)
def eval_model(model):
pred_series = model.predict(n=96)
plt.figure(figsize=(8, 5))
series_transformed.plot(label='actual')
pred_series.plot(label='forecast')
plt.title('MAPE: {:.2f}%'.format(mape(pred_series, val_transformed)))