Plot multiple time series same graph r11/8/2023 If you don't have that information, you can determine which frequencies are important by extracting features with Fast Fourier Transform. In this case you knew ahead of time which frequencies were important. This gives the model access to the most important frequency features. You can get usable signals by using sine and cosine transforms to clear "Time of day" and "Time of year" signals: day = 24*60*60ĭf = np.sin(timestamp_s * (2 * np.pi / day))ĭf = np.cos(timestamp_s * (2 * np.pi / day))ĭf = np.sin(timestamp_s * (2 * np.pi / year))ĭf = np.cos(timestamp_s * (2 * np.pi / year)) There are many ways you could deal with periodicity. Being weather data, it has clear daily and yearly periodicity. Similar to the wind direction, the time in seconds is not a useful model input. Start by converting it to seconds: timestamp_s = date_time.map(pd.Timestamp.timestamp) Similarly, the Date Time column is very useful, but not in this string form. The distribution of wind vectors is much simpler for the model to correctly interpret: plt.hist2d(df, df, bins=(50, 50), vmax=400) # Calculate the max wind x and y components. Right now the distribution of wind data looks like this: plt.hist2d(df, df, bins=(50, 50), vmax=400)īut this will be easier for the model to interpret if you convert the wind direction and velocity columns to a wind vector: wv = df.pop('wv (m/s)') Direction shouldn't matter if the wind is not blowing. Angles do not make good model inputs: 360° and 0° should be close to each other and wrap around smoothly. The last column of the data, wd (deg)-gives the wind direction in units of degrees. # The above inplace edits are reflected in the DataFrame.īefore diving in to build a model, it's important to understand your data and be sure that you're passing the model appropriately formatted data. There's a separate wind direction column, so the velocity should be greater than zero ( >=0). One thing that should stand out is the min value of the wind velocity ( wv (m/s)) and the maximum value ( max. Next, look at the statistics of the dataset: df.describe().transpose() Here is the evolution of a few features over time: plot_cols = # Slice, starting from index 5 take every 6th record.ĭate_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S') This tutorial will just deal with hourly predictions, so start by sub-sampling the data from 10-minute intervals to one-hour intervals: df = pd.read_csv(csv_path) This section of the dataset was prepared by François Chollet for his book Deep Learning with Python. For efficiency, you will use only the data collected between 20. These were collected every 10 minutes, beginning in 2003. This dataset contains 14 different features such as air temperature, atmospheric pressure, and humidity. This tutorial uses a weather time series dataset recorded by the Max Planck Institute for Biogeochemistry.
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