## How to plot a time series array, with confidence intervals displayed, in python?

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I have some time series which slowly increases, but over a short period of time they are very wavy. For example, the time series could look like:

[10 + np.random.rand() for i in range(100)] + [12 + np.random.rand() for i in range(100)] + [14 + np.random.rand() for i in range(100)]

I would like to plot the time series with a focus on the general trend, not on the small waves. Is there a way to plot the mean over a period of time surrounded with a stripe indicating the waves (the stripe should represent the confidence interval, where the data point could be in that moment)?

A simple plot would look like this:

The plot which I would like, with confidence intervals would look like this:

Is there an elegant way to do it in Python?

You could use `pandas`

function `rolling(n)`

to generate the mean and standard deviation values over `n`

consecutive points.

For the shade of the confidence intervals (represented by the space between standard deviations) you can use the function `fill_between()`

from `matplotlib.pyplot`

. For more information you could take a look over here, from which the following code is inspired.

time_series_df = pd.DataFrame(time_series_array) smooth_path = time_series_df.rolling(20).mean() path_deviation = 2 * time_series_df.rolling(20).std() plt.plot(smooth_path, linewidth=2) plt.fill_between(path_deviation.index, (smooth_path-2*path_deviation)[0], (smooth_path+2*path_deviation)[0], color='b', alpha=.1)

With the above code you obtain something like this:

**Time Series Data Visualization with Python,** 6 Ways to Plot Your Time Series Data with Python In this plot, time is shown on the x-axis with observation values along the y-axis. It can be helpful to compare line plots for the same interval, such as from day-to-day, month-to-month, but i got another error,'setting an array element with a sequence. In this tutorial, you will discover how to calculate and interpret prediction intervals for time series forecasts with Python. Specifically, you will learn: How to make a forecast with an ARIMA model and gather forecast diagnostic information. How to interpret a prediction interval for a forecast and configure different intervals.

Looks like, you're doubling the std twice. I guess it should be like this:

time_series_df = pd.DataFrame(time_series_array) smooth_path = time_series_df.rolling(20).mean() path_deviation = time_series_df.rolling(20).std() plt.plot(smooth_path, linewidth=2) plt.fill_between(path_deviation.index, (smooth_path-2*path_deviation)[0], (smooth_path+2*path_deviation)[0], color='b', alpha=.1)

**seaborn.lineplot,** It is possible to show up to three dimensions independently by using all three semantic types, but this style huename of variables in data or vector data, optional Draw a single line plot with error bands showing a confidence interval: from matplotlib.colors import LogNorm >>> ax = sns.lineplot(x="time", y="firing_rate", Selecting a time series forecasting model is just the beginning. Using the chosen model in practice can pose challenges, including data transformations and storing the model parameters on disk. In this tutorial, you will discover how to finalize a time series forecasting model and use it to make predictions in Python. After completing this tutorial, …

You can generate the smooth curve in various ways.

A simple approach is to use a moving average (average value of points in a sliding window). If you store your data in a Pandas dataframe, this can be plotted very easily. You can also compute the standard error for each point to get your confidence bands.

A another approach would be to fit a model to the data and use that to generate the smoothed curve. For example, you can do that using a Gaussian Process. This model can also produce the desired confidence band for each point. See this Scikit-learn example for more information.

**Comprehensive Confidence Intervals for Python Developers ,** Confidence interval tells you how confident you can be that the results depths, which give us the confidence interval plot of rock porosities shown in figure (2). Different libraries make different assumption about an input array. In time series, all data points are aligned with respect to time, but random Is there a way to plot the mean over a period of time surrounded with a stripe indicating the waves (the stripe should represent the confidence interval, where the data point could be in that moment)? A simple plot would look like this: The plot which I would like, with confidence intervals would look like this:

**Seaborn Data Visualization: Time Series Plot,** Let's cycle through each level and concat them all together at the end. dataArray = [] for level in levels: Time Series using Axes of type date¶ Time series can be represented using either plotly.express functions (px.line, px.scatter, px.bar etc) or plotly.graph_objects charts objects (go.Scatter, go.Bar etc). For more examples of such charts, see the documentation of line and scatter plots or bar charts.

**statsmodels.graphics.tsaplots.plot_acf,** Plot the autocorrelation function Array of time-series values If a number is given, the confidence intervals for the given level are returned. Teams. Q&A for Work. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

**Creating A Time Series Plot With Seaborn And pandas,** Time Series Splot With Confidence Interval Lines But No Lines. sns.tsplot([df.deaths_regiment_1, df.deaths_regiment_2, df.deaths_regiment_3, The Summary of an ARMA prediction for time series (print arma_mod.summary()) shows some numbers about the confidence interval.Is it possible to use these numbers as prediction intervals in the plot which shows predicted values?