h3ik0th

import pandas as pd
import numpy as np
from timeit import Timer


# create a dataframe with N rows
def create_df(N):
    df = pd.DataFrame({
        'A' : ['red', 'blue', 'green', 'white'] * int(N/4),
        'B' : np.random.randint(0, 100, N),

h3ik0th

# plot the forecast
plt.figure(100, figsize=(20, 7))
sns.set(font_scale=1.3)
p = sns.lineplot(x = "time", y = "Q50", data = dfY, palette="coolwarm")
sns.lineplot(x = "time", y = "Actual", data = dfY, palette="coolwarm")
plt.legend(labels=["forecast median price Q50", "actual price"])
p.set_ylabel("price")
p.set_xlabel("")
end = ts_tpred.end_time()
p.set_title("energy price until {} (test set + {} hours out-of-sample)".format(end, k));

h3ik0th

# retrieve forecast series for chosen quantiles, 
# inverse-transform each series,
# insert them as columns in a new dataframe dfY
q50_RMSE = np.inf
q50_MAPE = np.inf
ts_q50 = None
pd.options.display.float_format = '{:,.2f}'.format
dfY = pd.DataFrame()
#dfY["Actual"] = TimeSeries.pd_series(ts_test)
 

h3ik0th

# forecast from end of training set until k hours beyond end of test set
ts_tpred = model.predict(   n=n_FC, 
                            future_covariates=cov_t_fut, 
                            num_samples=N_SAMPLES, 
                            verbose=True, 
                            n_jobs=N_JOBS)
print("start:", ts_tpred.start_time(), "; end:",ts_tpred.end_time())

h3ik0th

# choose forecast horizon: k hours beyond end of test set
k = 12   

n_FC = k + len(ts_ttest)   # length of test set + k hours
print("forecast beyond end of training set:", n_FC, 
      "hours beyond", ts_ttrain.end_time())

# last 24 hours of feature covariates available => copy them to future 24 hours:
covF_t_fut = covF_t.concatenate(    other=covF_t.tail(size=24), 
                                    ignore_time_axes=True)

h3ik0th

# plot the forecast
plt.figure(100, figsize=(20, 7))
sns.set(font_scale=1.3)
p = sns.lineplot(x = "time", y = "Q50", data = dfY, palette="coolwarm")
sns.lineplot(x = "time", y = "Actual", data = dfY, palette="coolwarm")
plt.legend(labels=["forecast median price Q50", "actual price"])
p.set_ylabel("price")
p.set_xlabel("")
p.set_title("energy price (test set)");

h3ik0th

# retrieve forecast series for chosen quantiles, 
# inverse-transform each series,
# insert them as columns in a new dataframe dfY
q50_RMSE = np.inf
q50_MAPE = np.inf
ts_q50 = None
pd.options.display.float_format = '{:,.2f}'.format
dfY = pd.DataFrame()
dfY["Actual"] = TimeSeries.pd_series(ts_test)

h3ik0th

# testing: generate predictions
ts_tpred = model.predict(   n=len(ts_ttest), 
                            num_samples=N_SAMPLES,   
                            n_jobs=N_JOBS, 
                            verbose=True)

h3ik0th

# training: load a saved model or (re)train
if LOAD:
    print("have loaded a previously saved model from disk:" + mpath)
    model = TFTModel.load_model(mpath)                            # load previously model from disk 
else:
    model.fit(  series=ts_ttrain, 
                future_covariates=cov_t, 
                val_series=ts_ttest, 
                val_future_covariates=cov_t, 
                verbose=True)

h3ik0th

model = TFTModel(   input_chunk_length=INLEN,
                    output_chunk_length=N_FC,
                    hidden_size=HIDDEN,
                    lstm_layers=LSTMLAYERS,
                    num_attention_heads=ATTH,
                    dropout=DROPOUT,
                    batch_size=BATCH,
                    n_epochs=EPOCHS,                        
                    nr_epochs_val_period=VALWAIT, 
                    likelihood=QuantileRegression(QUANTILES),