Overview

• Dataset: Consisted of 14 different features such as air temperature, atmospheric pressure, and humidity which collected every 10 minutes, beginning in 2003. For efficiency data collected between 2009 and 2016 are used.  weather time series dataset recorded by the Max Planck Institute for Biogeochemistry
• Library: TensorFlow with RNNs and CNNs
• Covered two main part: A single feature, and All feature
• Forecast multiple steps: Single-shot (Make the predictions all at once), and Autoregressive (Make one prediction at a time and feed the output back to the model)
• codebase: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/time_series.ipynb#scrollTo=ojHE-iCCWIhz

Libraries:

1. Importing Libraries

import os
import datetime
import IPython
import IPython.display
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import tensorflow as tf

• os: Provides a way of interacting with the operating system, allowing you to access files, directories, and environment variables.
• datetime: Deals with dates and times, useful for timestamping or logging model performance over time.
• IPython and IPython.display: IPython is a rich environment for interactive computing. IPython.display helps to display rich output like images, audio, and videos in Jupyter notebooks.
• matplotlib as mpl and matplotlib.pyplot as plt: These are used for plotting and visualizing data. mpl is the base library, and pyplot is the simplified interface.
• numpy as np: Provides support for arrays and matrices and is essential for numerical computations in Python.
• pandas as pd: A powerful data manipulation library, mainly used for handling structured data in the form of DataFrames.
• seaborn as sns: Built on top of matplotlib, Seaborn makes it easier to create attractive and informative statistical graphics.
• tensorflow as tf: TensorFlow is an open-source machine learning library, widely used for building and training machine learning models.

2. Setting Plot Configurations