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() To get the future behavior, use `series.loc`. To retain the old behavior, use `series.iloc`. In a future version, this will be treated as *label-based* indexing, consistent with e.g. tmpfs/tmp/ipykernel_52317/637349053.py:7: FutureWarning: The behavior of `series` with an integer-dtype index is deprecated. 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. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. 02:53:22.641851: W tensorflow/compiler/tf2tensorrt/utils/py_:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. 02:53:22.641842: W tensorflow/compiler/xla/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libnvinfer_plugin.so.7' dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 02:53:22.641729: W tensorflow/compiler/xla/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libnvinfer.so.7' dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory Autoregressive: Make one prediction at a time and feed the output back to the model.Single-shot: Make the predictions all at once.This is covered in two main parts, with subsections: It builds a few different styles of models including Convolutional and Recurrent Neural Networks (CNNs and RNNs). This tutorial is an introduction to time series forecasting using TensorFlow.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |