Prophet is a time series library developed by Facebook. Prophet is particularly adept at modelling a time series with significant seasonal trends, as well as those with various “changepoints” present, i.e. structural breaks in the time series.
However, trend, seasonality and changepoints can often be more defined across a longer time series, as longer-term characteristics of the series become more apparent.
For this example, Prophet is used to conduct forecasts across two time series.
While it is debatable as to what specifically consists of a “short” and “long” term time series, it will be assumed for this purpose that the weekly hotel cancellations is a short-term time series, given that there is two years of data — which is likely long enough to extrapolate long-term trend and seasonal factors.
This example is elaborated on further in a previous article titled, “Time Series Analysis with Prophet: Air Passenger Data”. The full details of the analysis as well as the relevant link to the GitHub repository containing the dataset and Jupyter Notebook can be found there.
A summary of the results are included here for illustration.
Prophet was able to outperform an ARIMA model in forecasting air passenger numbers.
However, air passenger data typically follows a predictable seasonal pattern — with passenger numbers rising throughout the summer and then descending through the winter months. Moreover, the data under analysis was over the course of a decade, which allows Prophet to factor in long-term trends and seasonality shifts into the analysis.
#machine-learning #data-science #statistics #timeseries
Short news apps are the future, and if they will play a defining role in changing the way consumers consume their content and how the news presenters write their report.
If you want to build an app for short news then you can check out some professional app development companies for your app project As we head into the times where mobile applications and smartphones will be used for anything and everything, the short news applications will allow the reader to choose from various options and read what they want to read.
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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
In this article, using Telegram and Python, I will show how to build a friendly Bot with multiple functions that can chat with question-answering conversations (short-term information) and store user data to recall in the future (long-term information).
All this started because a friend of mine yelled at me for not remembering her birthday. I don’t know if that has ever happened to you. So I thought I could pretend I remember birthdays while I actually have a Bot doing it for me. Now I know what you’re thinking, why building something from scratch instead of using one of the millions of calendar apps around? And you’re right, but for nerds like us … what’s the fun in that?
Through this tutorial, I will explain step by step how to build an intelligent Telegram Bot with Python and MongoDB and how to deploy it for free with Heroku and Cron-Job,using my Dates Reminder Bot as an example(link below).
I will present some useful Python code that can be easily applied in other similar cases (just copy, paste, run) and walk through every line of code with comments so that you can replicate this example (link to the full code below).
In particular, I will go through:
#artificial-intelligence #programming #web-development #chatbots #engineering #build & deploy a telegram bot with short-term and long-term memory