# Slope of time series python

Last Updated on August 28, Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting. In this tutorial, you will discover time series decomposition and how to automatically split a time series into its components with Python. Discover how to prepare and visualize time series data and develop autoregressive forecasting models in my new bookwith 28 step-by-step tutorials, and full python code.

A useful abstraction for selecting forecasting methods is to break a time series down into systematic and unsystematic components. A given time series is thought to consist of three systematic components including level, trend, seasonality, and one non-systematic component called noise.

A multiplicative model is nonlinear, such as quadratic or exponential. Changes increase or decrease over time. Decomposition is primarily used for time series analysis, and as an analysis tool it can be used to inform forecasting models on your problem. It provides a structured way of thinking about a time series forecasting problem, both generally in terms of modeling complexity and specifically in terms of how to best capture each of these components in a given model. Each of these components are something you may need to think about and address during data preparation, model selection, and model tuning.

You may address it explicitly in terms of modeling the trend and subtracting it from your data, or implicitly by providing enough history for an algorithm to model a trend if it may exist. You may or may not be able to cleanly or perfectly break down your specific time series as an additive or multiplicative model.

Real-world problems are messy and noisy. There may be additive and multiplicative components. There may be an increasing trend followed by a decreasing trend. There may be non-repeating cycles mixed in with the repeating seasonality components. Nevertheless, these abstract models provide a simple framework that you can use to analyze your data and explore ways to think about and forecast your problem.

There are methods to automatically decompose a time series. It requires that you specify whether the model is additive or multiplicative. Both will produce a result and you must be careful to be critical when interpreting the result.

Best Fit Slope - Machine Learning Tutorial with python

A review of a plot of the time series and some summary statistics can often be a good start to get an idea of whether your time series problem looks additive or multiplicative.

The result object contains arrays to access four pieces of data from the decomposition. For example, the snippet below shows how to decompose a series into trend, seasonal, and residual components assuming an additive model. The result object provides access to the trend and seasonal series as arrays. It also provides access to the residuals, which are the time series after the trend, and seasonal components are removed.

Finally, the original or observed data is also stored. These four time series can be plotted directly from the result object by calling the plot function. For example:. We can create a time series comprised of a linearly increasing trend from 1 to 99 and some random noise and decompose it as an additive model. If a Pandas Series object is provided, this argument is not required. Running the example creates the series, performs the decomposition, and plots the 4 resulting series.

We can see that the entire series was taken as the trend component and that there was no seasonality. We can also see that the residual plot shows zero.

This is a good example where the naive, or classical, decomposition was not able to separate the noise that we added from the linear trend. The naive decomposition method is a simple one, and there are more advanced decompositions available, like Seasonal and Trend decomposition using Loess or STL decomposition.Get the latest tutorials on SysAdmin and open source topics.

Write for DigitalOcean You get paid, we donate to tech non-profits. DigitalOcean Meetups Find and meet other developers in your city. Become an author. Time-series analysis belongs to a branch of Statistics that involves the study of ordered, often temporal data.

When relevantly applied, time-series analysis can reveal unexpected trends, extract helpful statistics, and even forecast trends ahead into the future. For these reasons, it is applied across many fields including economics, weather forecasting, and capacity planning, to name a few. In this tutorial, we will introduce some common techniques used in time-series analysis and walk through the iterative steps required to manipulate, visualize time-series data.

This guide will cover how to do time-series analysis on either a local desktop or a remote server. Working with large datasets can be memory intensive, so in either case, the computer will need at least 2GB of memory to perform some of the calculations in this guide.

If you do not have it already, you should follow our tutorial to install and set up Jupyter Notebook for Python 3. We will leverage the pandas library, which offers a lot of flexibility when manipulating data, and the statsmodels library, which allows us to perform statistical computing in Python. Used together, these two libraries extend Python to offer greater functionality and significantly increase our analytical toolkit.

### Time Series Analysis in Python – A Comprehensive Guide with Examples

Like with other Python packages, we can install pandas and statsmodels with pip. We will call it timeseries and then move into the directory. If you call the project a different name, be sure to substitute your name for timeseries throughout the guide. We can now install pandasstatsmodelsand the data plotting package matplotlib. Their dependencies will also be installed:. This will open a notebook which allows us to load the required libraries notice the standard shorthands used to reference pandasmatplotlib and statsmodels.

At the top of our notebook, we should write the following:. Conveniently, statsmodels comes with built-in datasets, so we can load a time-series dataset straight into memory. We can bring in this data as follows:. You may have noticed that the dates have been set as the index of our pandas DataFrame. When working with time-series data in Python we should ensure that dates are used as an index, so make sure to always check for that, which we can do by running the following:.

This can be obtained by using the convenient resample function, which allows us to group the time-series into buckets 1 monthapply a function on each group meanand combine the result one row per group.

Here, the term MS means that we group the data in buckets by months and ensures that we are using the start of each month as the timestamp:. An interesting feature of pandas is its ability to handle date stamp indices, which allow us to quickly slice our data.

For example, we can slice our dataset to only retrieve data points that come after the year :. Or, we can slice our dataset to only retrieve data points between October and October :. With our data properly indexed for working with temporal data, we can move onto handling values that may be missing. Real world data tends be messy. As we can see from the plot, it is not uncommon for time-series data to contain missing values.

The simplest way to check for those is either by directly plotting the data or by using the command below that will reveal missing data in ouput:.

We can do this in pandas using the fillna command. For simplicity, we can fill in missing values with the closest non-null value in our time series, although it is important to note that a rolling mean would sometimes be preferable. With missing values filled in, we can once again check to see whether any null values exist to make sure that our operation worked:.

After performing these operations, we see that we have successfully filled in all missing values in our time series. When working with time-series data, a lot can be revealed through visualizing it. A few things to look out for are:.In the Facebook Live code along session on the 4th of January, we checked out Google trends data of keywords 'diet', 'gym' and 'finance' to see how they vary over time.

We asked ourselves if there could be more searches for these terms in January when we're all trying to turn over a new leaf? In this tutorial, you'll go through the code that we put together during the session step by step. You're not going to do much mathematics but you are going to do the following:.

The emphasis of this tutorial will be squarely on a visual exploration of the dataset in question. So the question remains: could there be more searches for these terms in January when we're all trying to turn over a new leaf?

Let's find out by going here and checking out the data. Note that this tutorial is inspired by this FiveThirtyEight piece. You can also download the data as a. You'll do this now. Let's get it! To start, you'll import some packages: in this case, you'll make use of numpypandasmatplotlib and seaborn. Alternatively, you can also switch to the Seaborn defaults with sns. Import data that you downloaded with.

Note that you add the skiprows argument to skip the first row at the start of the file. You can also use the. Now that you've imported your data from Google trends and had a brief look at it, it's time to wrangle your data and get it into the form you want to prepare it for data analysis.

The first thing that you want to do is rename the columns of your DataFrame df so that they have no whitespaces in them. There are multiple ways to do this, but for now, you'll reassign to df. Next, you'll turn the 'month' column into a DateTime data type and make it the index of the DataFrame.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.

It only takes a minute to sign up. I am extracting features from time series data for input into a classification algorithm, for example I'm extracting average and variance from inputX. For input Y, I have graphed the data and have seen that for class A, it can be seen that there is an upwards slope, and for class B, it can be seen that there is a downward slope, for class C there is no slope, the line is more or less straight.

Post as a guest Name. Email Required, but never shown. The Overflow Blog.Additive models for time series modeling. Time series are one of the most common data types encountered in daily life. Financial prices, weather, home energy usage, and even weight are all examples of data that can be collected at regular intervals. Almost every data scientist will encounter time series in their daily work and learning how to model them is an important skill in the data science toolbox.

One powerful yet simple method for analyzing and predicting periodic data is the additive model. The idea is straightforward: represent a time-series as a combination of patterns at different scales such as daily, weekly, seasonally, and yearly, along with an overall trend.

Your energy use might rise in the summer and decrease in the winter, but have an overall decreasing trend as you increase the energy efficiency of your home.

The following image shows an additive model decomposition of a time-series into an overall trend, yearly trend, and weekly trend. This post will walk through an introductory example of creating an additive model for financial time-series data using Python and the Prophet forecasting package developed by Facebook. Along the way, we will cover some data manipulation using pandas, accessing financial data using the Quandl library andand plotting with matplotlib. I have included code where it is instructive, and I encourage anyone to check out the Jupyter Notebook on GitHub for the full analysis.

This introduction will show you all the steps needed to start modeling time-series on your own! Disclaimer: Now comes the boring part when I have to mention that when it comes to financial data, past performance is no indicator of future performance and you cannot use the methods here to get rich. I chose to use stock data because it is easily available on a daily frequency and fun to play around with.

If you really want to become wealthy, learning data science is a better choice than playing the stock market! Quandl can be installed with pip from the command line, lets you access thousands of financial indicators with a single line of Python, and allows up to 50 requests a day without signing up. If you sign up for a free account, you get an api key that allows unlimited requests.

First, we import the required libraries and get some data. Quandl automatically puts our data into a pandas dataframe, the data structure of choice for data science. You can also specify a date range. There is an almost unlimited amount of data on quandl, but I wanted to focus on comparing two companies within the same industry, namely Tesla and General Motors. Tesla is a fascinating company not only because it is the first successful American car start-up in yearsbut also because at times in it was the most valuable car company in America despite only selling 4 different cars.

The other contender for the title of most valuable car company is General Motors which recently has shown signs of embracing the future of cars by building some pretty cool but not cool-looking all-electric vehicles.

## Time Series Data Visualization with Python

We could easily have spent hours searching for this data and downloading it as csv spreadsheet files, but instead, thanks to quandl, we have all the data we need in a few seconds!

This will also allows us to look for outliers or missing values that need to be corrected. Pandas dataframes can be easily plotted with matplotlib.

I also find matplotlib to be unintuitive and often copy and paste examples from Stack Overflow or documentation to get the graph I want. Quandl does not have number of shares data, but I was able to find average yearly stock shares for both companies with a quick Google search.

Is is not exact, but will be accurate enough for our analysis. Sometimes we have to make do with imperfect data! We do the same process with the GM data and then merge the two. Merging is an essential part of a data science workflow because it allows us to join datasets on a shared column.

In this case, we have stock prices for two different companies on the same dates and we therefore want to join the data on the date column. After merging, we rename the columns so we know which one goes with which car company.

The market cap is in billions of dollars. We can see General Motors started off our period of analysis with a market cap about 30 times that of Tesla! Do things stay that way over the entire timeline?By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I'm a newbie in python and machine learning. I'm trying to use gradient descent method linear regression maybe to get a slope from temperature and time series of graph in python 2. I'm getting temperature and time values from OpenTSDB, and time value is originally appeared as unix time but I changed it to string with using like below.

Gradient descent is an alogrithm to find extremes minimum or maximum of a function and the problem is, you do not have a function. All you have is a sequence of points. Now you might try to fit a polynomial to your points and compute the derivative of that function, but that probably would not be too accurate, given your data is 'bumpy', or you would have to use a high degree polynomial.

The second option is linear interpolation: In plain words, take two points and fit a line between them and calculate the slope of that line. Learn more. Is there any ways to get a slope in time series using gradient descent from python? Ask Question.

Asked 3 years ago. Active 3 years ago. Viewed times. Active Oldest Votes. The real question is: what do you need to accomplish in the first place? Robin Nemeth Robin Nemeth 2, 1 1 gold badge 17 17 silver badges 27 27 bronze badges. Thanks for the answer, then I might use what you recommended in second option. Out of curiosity - what are you trying to do with the slope? I want to analyze the values of slope between time and temperatureand what kind of relationship is there when people are in the room or not.

Also, I have a simple question, I know that the data is 'bumpy'. Can I also use Gaussian filtering method in that data? Sign up or log in Sign up using Google.Linear regression is always a handy option to linearly predict data. At first glance, linear regression with python seems very easy. If you use pandas to handle your data, you know that, pandas treat date default as datetime object.

The datetime object cannot be used as numeric variable for regression analysis. So, whatever regression we apply, we have to keep in mind that, datetime object cannot be used as numeric value. The idea to avoid this situation is to make the datetime object as numeric value. Then do the regression. During plotting the regression and actual data together, make a common format for the date for both set of data. In this case, I have made the data for x axis as datetime object for both actual and regression value.

The pandas library is imported for data handling. Numpy for array handling. Os for file directory. SciPy for linear regression. Matplotlib for plotting. This line is only useful for those who use jupyter notebook.

Now let us start linear regression in python using pandas and other simple popular library. It has the time series Arsenic concentration data. If your data is in another format, there are various other functions available in pandas library. For time series data it is very important to make the index column as date. For initial impression we should view the data to check whether everything is ok with the data or not. As you can see, in my data set there are a lot of empty cells.

Pandas imports empty cells as NaN. So, before any kind of analysis or plotting we should keep this in mind. Now our xy data are ready to pass through the linear regression analysis.

Now we will predict some y values within our data range. We will also save the unix numeric date values in different variables as datetime object. Now all our data and predicted data sets are ready to plot in same date time axis. For data analysis you can checkout my fiverr gig. The link goes below. Thanks very much Mohammed, I have been looking for this, very useful for me to trend time-series temperature rise.