NeuralProphet can make forecasts for time series with sub-daily observations by passing in a dataframe with timestamps in the ds column. The format of the timestamps should be
YYYY-MM-DD HH:MM:SS - see the example csv here. When sub-daily data are used, daily seasonality will automatically be fit.
Here we fit NeuralProphet to data with 5-minute resolution (daily temperatures at Yosemite).
if "google.colab" in str(get_ipython()): # uninstall preinstalled packages from Colab to avoid conflicts !pip uninstall -y torch notebook notebook_shim tensorflow tensorflow-datasets prophet torchaudio torchdata torchtext torchvision !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")
data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/" df = pd.read_csv(data_location + "yosemite_temps.csv")
Now we will attempt to forecast the next 7 days. The
5min data resulution means that we have
60/5*24=288 daily values. Thus, we want to forecast
7*288 periods ahead.
Using some common sense, we set: * First, we disable weekly seasonality, as nature does not follow the human week’s calendar. * Second, we disable changepoints, as the dataset only contains two months of data
m = NeuralProphet( n_changepoints=0, weekly_seasonality=False, ) m.set_plotting_backend("plotly-static") metrics = m.fit(df, freq="5min") future = m.make_future_dataframe(df, periods=7 * 288, n_historic_predictions=True) forecast = m.predict(future) m.plot(forecast)
The daily seasonality seems to make sense, when we account for the time being recorded in GMT, while Yosemite local time is GMT-8.
Improving trend and seasonality#
As we have
288 daily values recorded, we can increase the flexibility of
daily_seasonality, without danger of overfitting.
Further, we may want to re-visit our decision to disable changepoints, as the data clearly shows changes in trend, as is typical with the weather. We make the following changes: * increase the
changepoints_range, as the we are doing a short-term prediction * inrease the
n_changepoints to allow to fit to the sudden changes in trend * carefully regularize the trend changepoints by setting
trend_reg in order to avoid overfitting
m = NeuralProphet( changepoints_range=0.95, n_changepoints=50, trend_reg=1, weekly_seasonality=False, daily_seasonality=10, ) m.set_plotting_backend("plotly-static") metrics = m.fit(df, freq="5min") future = m.make_future_dataframe(df, periods=60 // 5 * 24 * 7, n_historic_predictions=True) forecast = m.predict(future) m.plot(forecast)