Prophet: Automatic Forecasting Procedure
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
Prophet is a CRAN package so you can use
After installation, you can get started!
You can also choose an experimental alternative stan backend called
cmdstanr. Once you've installed
prophet, follow these instructions to use
cmdstanr instead of
rstan as the backend:
# R # We recommend running this is a fresh R session or restarting your current session install.packages(c("cmdstanr", "posterior"), repos = c("https://mc-stan.org/r-packages/", getOption("repos"))) # If you haven't installed cmdstan before, run: cmdstanr::install_cmdstan() # Otherwise, you can point cmdstanr to your cmdstan path: cmdstanr::set_cmdstan_path(path = <your existing cmdstan>) # Set the R_STAN_BACKEND environment variable Sys.setenv(R_STAN_BACKEND = "CMDSTANR")
If you have custom Stan compiler settings, install from source rather than the CRAN binary.
Prophet is on PyPI, so you can use
pip to install it. From v0.6 onwards, Python 2 is no longer supported. As of v1.0, the package name on PyPI is "prophet"; prior to v1.0 it was "fbprophet".
# Install pystan with pip before using pip to install prophet # pystan>=3.0 is currently not supported pip install pystan==188.8.131.52 pip install prophet
The default dependency that Prophet has is
pystan. PyStan has its own installation instructions. Install pystan with pip before using pip to install prophet.
You can also choose a (more experimental) alternative stan backend called
cmdstanpy. It requires the CmdStan command line interface and you will have to specify the environment variable
STAN_BACKEND pointing to it, for example:
# bash $ CMDSTAN=/tmp/cmdstan-2.22.1 STAN_BACKEND=CMDSTANPY pip install prophet
Note that the
CMDSTAN variable is directly related to
cmdstanpy module and can be omitted if your CmdStan binaries are in your
It is also possible to install Prophet with two backends:
# bash $ CMDSTAN=/tmp/cmdstan-2.22.1 STAN_BACKEND=PYSTAN,CMDSTANPY pip install prophet
After installation, you can get started!
If you upgrade the version of PyStan installed on your system, you may need to reinstall prophet (see here).
conda install gcc to set up gcc. The easiest way to install Prophet is through conda-forge:
conda install -c conda-forge prophet.
On Windows, PyStan requires a compiler so you'll need to follow the instructions. The easiest way to install Prophet in Windows is in Anaconda.
Make sure compilers (gcc, g++, build-essential) and Python development tools (python-dev, python3-dev) are installed. In Red Hat systems, install the packages gcc64 and gcc64-c++. If you are using a VM, be aware that you will need at least 4GB of memory to install prophet, and at least 2GB of memory to use prophet.
cmdstanpybackend now available in Python
Predicting stock prices is a difficult task. Several factors can affect the price of the stock which is not always easy to accommodate in a model. There is no model in the world currently which can accurately predict the stock prices and there might never be one owing to the reasons mentioned above. Facebook has given a “state of the art model” and “easy to use” and a wide range of hyperparameter tuning options to give somewhat accurate predictions.
As mentioned above, we have a dataset that has stock prices for New Germany Fund from the year 2013 to 2018. Now as we import the data and see it for the first time, we see that it is not sorted in the ascending order of the dates, This is a major issue as forecasted values are more likely to depend on the immediate past entries rather than entries before.
stock_prices['DATE'] = pd.to_datetime(stock_prices["DATE"]) stock_prices = stock_prices.sort_values(by="DATE")
After this, we plot the values of the opening price by date.
As you can see there is a sudden drop in values from 2013 to 2014 which is very unusual. A possible reason for this is that there may be very few values for the year 2013. We check that using the following code.
stock_prices = stock_prices[stock_prices.Year == 2013]
The above code results in a dataset with only 3 entries. We remove these values.
stock_prices = stock_prices[stock_prices.Year != 2013]
The data finally looks like:
We also need to set the index of our dataset as the date, but we can’t access the date as it is now a Dataframe index. To resolve this issue, we will first create a copy of the Date column.
stock_prices[‘date’] = stock_prices[‘DATE’] stock_prices.set_index("DATE", inplace = True)
The autocorrelation gives us insight into the seasonality of the model. In case the correlation value is high for a certain number of lags, that lag number is the seasonality.
Lag of value one corresponds to one day as the time step in our dataset is a day.
Evident from the below plot, the correlation is high for lags close to 0. The value of autocorrelation seems to decrease for a higher value of lags. Implying that as such, there is no seasonality within our data.
Autocorrelation vs Lags
We further gain insight into the yearly growth in data. The year 2017 has the largest area, hence the most growth.
Growth vs Years
#time-series-forecasting #prophet #stock-prediction #forecasting #machine-learning #deep learning
This tutorial was created to democratize data science for business users (i.e., minimize usage of advanced mathematics topics) and alleviate personal frustration we have experienced on following tutorials and struggling to apply that same tutorial for our needs. Considering this, our mission is as follows:
#python #data-science #machine-learning-ai #forecasting #prophet
Forecasting future demand is a fundamental business problem and any solution that is successful in tackling this will find valuable commercial applications in diverse business segments. In the retail context, Demand Forecasting methods are implemented to make decisions regarding buying, provisioning, replenishment, and financial planning. Some of the common time-series methods applied for Demand Forecasting and provisioning include Moving Average, Exponential Smoothing, and ARIMA. The most popular models in Kaggle competitions for time-series forecasting have been Gradient Boosting models that convert time-series data into tabular data, with lag terms in the time-series as ‘features’ or columns in the table.
The Facebook Prophet model is a type of GAM (Generalized Additive Model) that specializes in solving business/econometric — time-series problems. My objective in this project was to apply and investigate the performance of the Facebook Prophet model for Demand Forecasting problems and to this end, I used the Kaggle M5- Demand Forecasting Competition Dataset and participated in the competition. The competition aimed to generate point forecasts 28 days ahead at a product- store level.
The dataset involves unit sales of 3049 products and is classified into 3 product categories (Hobbies, Foods, and Household) and 7 departments. The products are sold in 10 stores located across 3 states (CA, TX, and WI). The diagram gives an overview of the levels of aggregations of the products. The competition data has been made available by Walmart.
Fig 1: Breakdown of the time-series Hierarchy and Aggregation Level 
Fig 2: Data Hierarchy Diagram 
The data range for Sales Data is from 2011–01–29 to 2016–06–19. Thus products have a maximum of 1941 days or 5.4 years worth of available data. (The Test dataset of 28 days is not included).
The datasets are divided into Calendar Data, Price Data, and Sales Data .
**Calendar Data — **contains columns, like date, weekday, month, year, and Snap-Days for the states TX, CA, and WI. Additionally, the table contains information on holidays and special events (like Superbowl) through its columns event_type1 and event_type2. The holidays/ special events are divided into cultural, national, religious, and sporting .
Price Data- The table consists of the columns — store, item, week, and price. It provides information on the price of an item at a particular store, in a particular week .
Sales Data — consists of validation and evaluation files. The evaluation file consists of sales for 28 extra days which can be used for model evaluation. The table provides information on the quantity sold for a particular item in a particular department, in a particular state, and store .
The data can be found in the link
Fig 3: Sales Qty.in Each State
Fig 4: Sales % in Each category
Fig 5: Sales % in Each State
As can be seen from the charts above, for every category, the highest number of sales occur in CA, followed by TX and WI. CA contributes to around 50% of Hobby sales. The sales distribution across categories in the three states is symmetric and the highest-selling categories ordered by descending order of sales in each state are Foods, Household, and Hobbies.
#time-series-forecasting #prophet #time-series-analysis #data-science #demand-for-evidence #data analysis
As part of an anomaly detection project, I have recently been able to use two very interesting open source products: Prophet released by the Core Data Science team by Facebook and Metaflow, an excellent framework by Netflix. I used Prophet, in a Metaflow flow, to create forecast models of time series. I decided to write this post to share my experience with these two products, creating a small machine learning project.
Being able to predict the future trend of a time series is very useful in many applications, from the world of finance to sales. For example, we try to predict the direction of the stock market or the correct supply of resources. This post does not set such ambitious goals, but only wants to explore the possibilities offered by Prophet by creating a forecast model that determines the future trend of daily temperatures. To train the model, I used a dataset that collects the minimum daily temperatures over 10 years (1981–1990) in the city of Melbourne, Australia. The source of the data is the Australian Bureau of Meteorology.
The entire source code of the project is available in this git repository
Let’s analyze our dataset with a simple notebook. We use Python and Pandas to load the CSV file.
#aws-batch #metaflow #time-series-forecasting #prophet #aws
I used to use Rob J Hyndman’s
[fpp2](https://cran.r-project.org/web/packages/fpp2/index.html) forecasting package. Quite a lot. Still it’s my go-to forecasting library. The reason I like it so much is that it comes with extensive coverage of forecasting techniques and an invaluable open access book that has all the theories going into forecasting. Pretty much everything you need for academic research on time series is there.
But that’s also the downside of the package, it’s not beginner-friendly. Who wants to build a car just to drive it on the road?
Then Facebook Prophet came along.
Prophet made unbelievable simplification to forecasting exercise. You can use it out of the box without needing to understand a lot of theories, as you are about to see below.
The package is very intuitive to use and is especially powerful for business forecasting. You can even specify weekends, special days and events (e.g. Superbowl) that impact business activities.
Cherry on top, Prophet is available in both python and R programming language!
Let’s do a quick demo.
I’m doing it in Python, so all you need is
pandas package for manipulating data.
And of course
## improt libraries import pandas as pd from fbprophet import Prophet
The dataset I’m going to use is a time series consisting of daily minimum temperature recorded for 10 years between 1981 and 1990.
## import data df = pd.read_csv("https://bit.ly/3hJwIm0") ## check out first few rows df.head()
As you can see, the datarame has just two columns, one on the time dimension and the other on observations.
Some data formatting is needed.
Prophet requires that the datetime column is named as “ds” and the observation column as “y”.
#time-series-analysis #data-science #machine-learning #facebook-prophet #forecasting