Time Series Analysis: Creating Synthetic Datasets

Time Series Analysis: Creating Synthetic Datasets

How to create time series datasets with different patterns

Time series is a sequence of values ordered in time. We may encounter time series data in pretty much any domain. Weather forecasts, exchange rates, sales data, sound waves are just a few examples. Time series can be any type of data that is represented as an ordered sequence.

In an earlier post , I covered the basic concepts in time series analysis. In this post, we will create time series data with different patterns. One advantage of synthetic datasets is that we can measure the performance of a model and have an idea about how it will perform with real life data.

The common patterns observed in a time series are:

• Trend: An overall upward or downward direction.
• Seasonality: Patterns that repeat observed or predictable intervals.
• White noise: Time series does not always follow a pattern or include seasonality. Some processes produce just random data. This kind of time series is called white noise.

Note: The patterns are not always smooth and usually include some kind of noise . Furthermore, a time series may include a combination of different patterns.

We will use numpy to generate arrays of values and matplotlib to plot the series. Let’s start with importing the required libraries:

import numpy as np
import matplotlib.pyplot as plt%matplotlib inline

We can define a function that takes the arrays as input and create plots:

def plot_time_series(time, values, label):
 plt.figure(figsize=(10,6))
 plt.plot(time, values)
 plt.xlabel("Time", fontsize=20)
 plt.ylabel("Value", fontsize=20)
 plt.title(label, fontsize=20)
 plt.grid(True)

Trend in Time Series

The first plot is the simplest one which is a time series with an upward trend. We create arrays for time and values with a slope. Then pass these arrays as arguments to our function:

time = np.arange(100)
values = time*0.4plot_time_series(time, values, "Upward Trend")