Test and CrossValidate

if 'google.colab' in str(get_ipython()):
    !pip install git+https://github.com/ourownstory/neural_prophet.git # may take a while
    #!pip install neuralprophet # much faster, but may not have the latest upgrades/bugfixes

import pandas as pd
from neuralprophet import NeuralProphet, set_log_level
set_log_level("ERROR")

Load data

data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/"
# df = pd.read_csv(data_location + "air_passengers.csv")

1. Basic: Train and Test a model

First, we show how to fit a model and evaluate it on a holdout set.

Note: before making any actual forecasts, re-fit the model on all data available, else you are greatly reducing your forecast accuracy!

1.1 Train-Test evaluation

m = NeuralProphet(seasonality_mode= "multiplicative", learning_rate = 0.1)

df = pd.read_csv(data_location + "air_passengers.csv")
df_train, df_test = m.split_df(df=df, freq="MS", valid_p=0.2)

metrics_train = m.fit(df=df_train, freq="MS")
metrics_test = m.test(df=df_test)

metrics_test

	SmoothL1Loss	MAE	MSE
0	0.007121	31.413279	1440.266357

1.2 Bonus: Predict into future

Before making any actual forecasts, re-fit the model on all data available, else you are greatly reducing your forecast accuracy!

metrics_train2 = m.fit(df=df, freq="MS")
forecast = m.predict(df)
fig = m.plot(forecast)

1.3 Bonus: Visualize training

If you installed the [live] version of NeuralProphet, you can additionally visualize your training progress and spot any overfitting by evaluating every epoch.

Note: Again, before making any predictions, re-fit the model with the entire data first.

m = NeuralProphet(seasonality_mode="multiplicative", learning_rate=0.1)

df = pd.read_csv(data_location + "air_passengers.csv")
df_train, df_test = m.split_df(df=df, freq="MS", valid_p=0.2)

metrics = m.fit(df=df_train, freq="MS", validation_df=df_test, plot_live_loss=True)

metrics.tail(1)

	SmoothL1Loss	MAE	MSE	RegLoss	SmoothL1Loss_val	MAE_val	MSE_val
307	0.000317	6.178366	64.096661	0.0	0.005971	28.745131	1207.533936

2. Time-series Cross-Validation

Time-series crossvalidation is also known as rolling origin backtest. In the first fold, we start with some data to train the model, and evaluate over the next fold_pct data points. In the next fold, the previous evaluation data is added to training, and evaluation starts later (forecast orgin ‘rolls’ forward), again measuring accuracy over the next section of data. We repeat this, until the final folds evaluation data reaches the end of the data.

Note: before making any actual forecasts, re-fit the model on all data available, else you are greatly reducing your forecast accuracy!

METRICS = ['SmoothL1Loss', 'MAE', 'MSE']
params = {"seasonality_mode": "multiplicative", "learning_rate": 0.1}

df = pd.read_csv(data_location + "air_passengers.csv")
folds = NeuralProphet(**params).crossvalidation_split_df(df, freq="MS", k=5, fold_pct=0.20, fold_overlap_pct=0.5)

metrics_train = pd.DataFrame(columns=METRICS)
metrics_test = pd.DataFrame(columns=METRICS)

for df_train, df_test in folds:
    m = NeuralProphet(**params)
    train = m.fit(df=df_train, freq="MS")
    test = m.test(df=df_test)
    metrics_train = metrics_train.append(train[METRICS].iloc[-1])
    metrics_test = metrics_test.append(test[METRICS].iloc[-1])

metrics_test.describe().loc[["mean", "std", "min", "max"]]

	SmoothL1Loss	MAE	MSE
mean	0.012356	22.827064	883.100308
std	0.012997	8.211850	508.476321
min	0.003793	10.655480	198.452499
max	0.034445	29.894016	1331.016846

2 Using Benchmark Framework

The benchmark framework is designed for a 2 phase evaluation pipeline. This is in most cases sufficient, particularly when using crossvalidation. For the remainder of this tutorial we will be using normal Train-Test or Cross-Validation evaluation setups.

Note: The Benchmarking Framework does currently not properly support auto-regression or lagged covariates with multiple step ahead forecasts.

from neuralprophet.benchmark import Dataset, NeuralProphetModel, SimpleExperiment, CrossValidationExperiment

2.1 SimpleExperiment

Let’s set up a train test experiment:

ts = Dataset(df = pd.read_csv(data_location + "air_passengers.csv"), name = "air_passengers", freq = "MS")
params = {"seasonality_mode": "multiplicative"}
exp = SimpleExperiment(
    model_class=NeuralProphetModel,
    params=params,
    data=ts,
    metrics=["MASE", "RMSE"],
    test_percentage=25,
)
result_train, result_test = exp.run()

# result_train
result_test

{'data': 'air_passengers',
 'model': 'NeuralProphet',
 'params': "{'seasonality_mode': 'multiplicative'}",
 'MASE': 0.5662454805745694,
 'RMSE': 28.829688840169517}

2.2 CrossValidationExperiment

Let’s crossvalidate:

ts = Dataset(df = pd.read_csv(data_location + "air_passengers.csv"), name = "air_passengers", freq = "MS")
params = {"seasonality_mode": "multiplicative"}
exp_cv = CrossValidationExperiment(
    model_class=NeuralProphetModel,
    params=params,
    data=ts,
    metrics=["MASE", "RMSE"],
    test_percentage=10,
    num_folds=3,
    fold_overlap_pct=0,
  )
result_train, result_test = exp_cv.run()

result_test

{'data': 'air_passengers',
 'model': 'NeuralProphet',
 'params': "{'seasonality_mode': 'multiplicative'}",
 'MASE': [0.5044070952378811, 0.3214836090858653, 0.6877225952888634],
 'RMSE': [21.220949835639722, 16.892885303566555, 32.749710284868435]}

3. Advanced: 3-Phase Train, Validate and Test procedure

Finally, in 3.1 and 3.2, we will do a 3-part data split to do a proper training, validation and test evaluation of your model. This setup is used if you do not want to bias your performance evaluation by your manual hyperparameter tuning. this is, however not common when working with time series, unless you work in academia. Crossvalidation is usually more than adequate to evaluate your model performance.

If you are confused by this, simply ignore this section and continue your forecasting life. Or if you got curious, read up on how to evaluate machine learning models to level up your skills.

3.1 Train, Validate and Test evaluation

m = NeuralProphet(seasonality_mode= "multiplicative", learning_rate = 0.1)

df = pd.read_csv(data_location + "air_passengers.csv")
# create a test holdout set:
df_train_val, df_test = m.split_df(df=df, freq="MS", valid_p=0.2)
# create a validation holdout set:
df_train, df_val = m.split_df(df=df_train_val, freq="MS", valid_p=0.2)

# fit a model on training data and evaluate on validation set.
metrics_train1 = m.fit(df=df_train, freq="MS")
metrics_val = m.test(df=df_val)

# refit model on training and validation data and evaluate on test set.
metrics_train2 = m.fit(df=df_train_val, freq="MS")
metrics_test = m.test(df=df_test)

metrics_train1["split"]  = "train1"
metrics_train2["split"]  = "train2"
metrics_val["split"] = "validate"
metrics_test["split"] = "test"
metrics_train1.tail(1).append([metrics_train2.tail(1), metrics_val, metrics_test]).drop(columns=['RegLoss'])

	SmoothL1Loss	MAE	MSE	split
324	0.000368	5.348336	46.359198	train1
649	0.000571	6.835680	72.002425	train2
0	0.005073	18.293804	639.260681	validate
0	0.002529	15.051163	318.605957	test

3.2 Train, Cross-Validate and Cross-Test evaluation

METRICS = ['SmoothL1Loss', 'MAE', 'MSE']
params = {"seasonality_mode": "multiplicative", "learning_rate": 0.1}

df = pd.read_csv(data_location + "air_passengers.csv")
folds_val, folds_test = NeuralProphet(**params).double_crossvalidation_split_df(df, freq="MS", k=5, valid_pct=0.10, test_pct=0.10)

metrics_train1 = pd.DataFrame(columns=METRICS)
metrics_val = pd.DataFrame(columns=METRICS)
for df_train1, df_val in folds_val:
    m = NeuralProphet(**params)
    train1 = m.fit(df=df_train, freq="MS")
    val = m.test(df=df_val)
    metrics_train1 = metrics_train1.append(train1[METRICS].iloc[-1])
    metrics_val = metrics_val.append(val[METRICS].iloc[-1])

metrics_train2 = pd.DataFrame(columns=METRICS)
metrics_test = pd.DataFrame(columns=METRICS)
for df_train2, df_test in folds_test:
    m = NeuralProphet(**params)
    train2 = m.fit(df=df_train2, freq="MS")
    test = m.test(df=df_test)
    metrics_train2 = metrics_train2.append(train2[METRICS].iloc[-1])
    metrics_test = metrics_test.append(test[METRICS].iloc[-1])

metrics_train2.describe().loc[["mean", "std"]]

	SmoothL1Loss	MAE	MSE
mean	0.000269	6.724876	74.517579
std	0.000023	0.090767	1.726542

metrics_val.describe().loc[["mean", "std"]]

	SmoothL1Loss	MAE	MSE
mean	0.009112	29.609479	1148.171173
std	0.007296	13.811697	919.264304

metrics_test.describe().loc[["mean", "std"]]

	SmoothL1Loss	MAE	MSE
mean	0.001103	14.406253	295.007996
std	0.001310	7.844408	337.153951