Granted, this is just an estimate. As we can see from the graph, several services were influenced by pandemic much more than others. I also calculate cross-elasticities of demand of Goods depending on Service prices. The rendered .nb.html files can be viewed in any modern web browser. A tag already exists with the provided branch name. When he was confident that he could satisfy these criteria, he moved forward with his plans to develop the PowerSki Jetboard. Ask them questions such as these:Karl Ulrich and Steven Eppinger, Product Design and Development, 2nd ed. We obtained hourly weather data from two different sources, a weather station located on Harvard campus and purchased weather data from weather stations located in Cambridge, MA. Work fast with our official CLI. Besides, there might be linear and non-linear constraints. The repository also comes with AzureML-themed notebooks and best practices recipes to accelerate the development of scalable, production-grade forecasting solutions on Azure. The following is a list of related repositories that you may find helpful. To do forecasts in Python, we need to create a time series. What does this means? But first, lets have a look at which economic model we will use to do our forecast. You have located a small storefront in a busy section of town. Demand-Forecasting-Models-for-Supply-Chain-Using-Statistical-and-Machine-Learning-Algorithms. Then, we run SQL queries to import the dataset in a tabular format as a SQL Database. Hosted on GitHub Pages Theme by orderedlist. Every service has a delivery Zone and Weight Range. You can obtain helpful information about product demand by talking with people in similar businesses and potential customers. You define the number of past values you want to consider for your forecast, the so called order of your AR term through the parameter p. Intgrated Moving Average (IMA): The integrated moving average part of an SARIMAX model comes from the fact that you take into account the past forecasting errors to correct your future forecasts. Note that html links are provided next to R examples for best viewing experience when reading this document on our github.io page. It doesnt have space for an eat-in restaurant, but it will allow customers to pick up their pizzas. Forecasting is known as an estimation/prediction of an actual value in future time span. Finally, we calculated the time data which include the hour of day, day of week, day of year, week of year, coshour=cos(hour of day * 2pi/24), and estimates of daily occupancy based on academic calendar. # model = ARIMA(train, order=(3,2,1)), 'C:/Users/Rude/Documents/World Bank/Forestry/Paper/Forecast/GDP_PastFuture.xlsx', "SARIMAX Forecast of Global Wood Demand (with GDP)". After youve identified a group of potential customers, your next step is finding out as much as you can about what they think of your product idea. Time Series Forecasting for Walmart Store Sales. If nothing happens, download GitHub Desktop and try again. What assumptions will you use in estimating sales (for example, the hours your pizza shop will be open)? You can find the data on this link. Before making a substantial investment in the development of a product, you need to ask yourself yet another question: are there enough customers willing to buy my product at a price that will allow me to make a profit? We follow 3 main steps when making predictions using time series forecasting in Python: Fitting the model Specifying the time interval Analyzing the results Fitting the Model Lets assume weve already created a time series object and loaded our dataset into Python. If you have any issues with the above setup, or want to find more detailed instructions on how to set up your environment and run examples provided in the repository, on local or a remote machine, please navigate to the Setup Guide. ARIMA/SARIMA model, Simple/Double/Triple Exponential Smoothing models, Prophet model. The predictions made are then used as an input to Power BI where predictions are being visualized. Now, lets say that your research turns up the fact that there are three million joggers older than sixty-five and that six hundred thousand of them live in Florida, which attracts 20 percent of all people who move when they retire.Alan Scher Zagier, Eyeing Competition, Florida Increases Efforts to Lure Retirees, Boston Globe, December 26, 2003, http://www.boston.com/news/nation/articles/2003/12/26/eyeing_competition_florida_increases_efforts_to_lure_retirees (accessed October 28, 2011). This project welcomes contributions and suggestions. The examples are organized according to use cases. Below we can do this exercise manually for an ARIMA(1,1,1) model: We can make our prediction better if we include variables into our model, that are correlated with global wood demand and might predict it. This project is about Deliveries prices optimization (or Services that go with sales), but you can use it for any retail area. These weather data contains extremely detailed weather datasets including outdoor temperature, humidity, wind speed, wind direction, solar radiation, atmospheric pressure, dehumidification, etc. You can also examine published industry data to estimate the total market for products like yours and estimate your. Please Predict M5 kaggle dataset, by LSTM and BI-LSTM and three optimal, bottom-up, top-down reconciliation approach. topic, visit your repo's landing page and select "manage topics.". The forecast user just needs to load data and choose the number of forecast periods to generate forecast and get lists of products that cannot be forecasts (stopped products and new products). The following table summarizes each forecasting scenario contained in the repository, and links available content within that scenario. Ive used a simple trick to decide, what time series have to be shortened by cutting the pandemic section out I checked if the number of orders from April to June does not differ significantly from the number of orders for the previous three months. Please Where do they buy them and in what quantity? Azure DataFactory, Azure Storage Account, Azure SQL Database, Azure SQL Server, Azure Databricks, Azure PowerBI. This folder contains Jupyter notebooks with Python examples for building forecasting solutions. The second one is about demand elasticities I estimate sales volume functions wrt prices. Time Series Forecasting Best Practices & Examples, Bike sharing prediction based on neural nets, Minimize forecast errors by developing an advanced booking model using Python. You signed in with another tab or window. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. Use Git or checkout with SVN using the web URL. sign in Lets know prepare the dataset for our purpose through grouping it by year. Thats why I decided to break this article into 3 pieces. The examples use the Tidyverts family of packages, which is a modern framework for time series analysis that builds on the widely-used Tidyverse family. Our target audience for this repository includes data scientists and machine learning engineers with varying levels of knowledge in forecasting as our content is source-only and targets custom machine learning modelling. Being realistic (but having faith in an excellent product), you estimate that youll capture 2 percent of the market during your first year. Work fast with our official CLI. Lets rely on data published by FAOSTAT for that purpose. Make sure that the selected Jupyter kernel is forecasting_env. His job, therefore, was to design a product that dealers would want to sell and enthusiasts would buy. Data Description from Kaggle: The dataset contains historical product demand for a manufacturing company with footprints globally. Lets upload the dataset to Python and merge it to our global wood demand: Lets see if both time-series are correlated: As you can see, GDP and Global Wood Demand are highly correlated with a value of nearly 1. Answering this question means performing one of the hardest tasks in business: forecasting demand for your proposed product. demand-forecasting topic, visit your repo's landing page and select "manage topics.". We've carried out a cleanup of large obsolete files to reduce the size of this repo. If you visited a few local restaurants and asked owners how many customers they served every day, youd probably learn enough to estimate the number of pizzas that youd serve during your first year. (New York: Irwin McGraw-Hill, 2000), 66; and Kathleen Allen, Entrepreneurship for Dummies (Foster, CA: IDG Books, 2001), 79. Time series forecasting is one of the most important topics in data science. You signed in with another tab or window. Please execute one of the following commands from the root of Forecasting repo based on your operating system. The following summarizes each directory of the Python best practice notebooks. Install Anaconda with Python >= 3.6. If nothing happens, download GitHub Desktop and try again. WebDemand Forecasting Data Card Code (4) Discussion (0) About Dataset One of the largest retail chains in the world wants to use their vast data source to build an efficient forecasting model to predict the sales for each SKU in its portfolio at its 76 different stores using historical sales data for the past 3 years on a week-on-week basis. To enable high-throughput forecasting scenarios, we have included examples for forecasting multiple time series with distributed training techniques such as Ray in Python, parallel package in R, and multi-threading in LightGBM. I develop a software that allows to : - Make commercial forecasts from a history - Compare several forecasting methods - Display the results (forecasts and comparison), Demand pattern recognition using k-means algorithm in Python. Applying a structural time series approach to California hourly electricity demand data. What factors would you consider in estimating pizza sales? The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. In addition, the example notebooks would serve as guidelines and showcase best practices and usage of the tools in a wide variety of languages. But not only. The utilities and examples provided are intended to be solution accelerators for real-world forecasting problems. Use the CopyData function in DataFactory to transfer data from Blob to SQL Database. Demand Forecasting is the key movement which pretty much controls any remaining exercises of Supply Chain Management. Forecast demands of products at a manufacturing company using a real-life dataset from Kaggle. Curated list of awesome supply chain blogs, podcasts, standards, projects, and examples. You can also learn a lot by talking with potential customers. This blog post gives an example of how to build a forecasting model in Python. We hope that the open source community would contribute to the content and bring in the latest SOTA algorithm. A different occupancy factor is assigned to school days, weekends and holidays. Autoregressive (AR): Autoregressive is a time series that depends on past values, that is, you autoregresse a future value on its past values. The examples are organized according Predicted Target value = Average of previous 5 years Production values - Average of previous 5 year Difference value, Where, Difference value = Production value - Target value. In the private sector we would like to know how certain markets relevant to our businesses develop in the next months or years to make the right investment decisions, and in the public sector we would like to know when to expect the next episode of economic decline. Time Series Forecasting Best Practices & Examples. topic page so that developers can more easily learn about it. First of all, we can plan the demand and supply based on the monthly sales forecasts. Physical Fitness Behavior (1987Present), http://www.americansportsdata.com/phys_fitness_trends1.asp (accessed October 28, 2011). Many reputed companies rely on demand forecasting to make major decisions related to production, expansions, sales, etc. The objective is to forecast demands for thousands of products at four central warehouses of a manufacturing company. All the services are linked through Azure DataFactory as an ETL pipeline. Thats it for the first part. Time Series Forecasting for the M5 Competition, Machine Learning for Retail Sales Forecasting Features Engineering. Answering this question means performing one of the hardest tasks in business: forecasting demand for your proposed product. WebObject Detection | Start up Profit Prediction | RealTime Eye Blink Detection | House Budget Prediction | Human Detection and Counting | Pencil Sketch of Photo | Predict Next Word with Python | Hand Gesture Recognition | Handwritten Character Recognition Recent Articles Thesis Assistance Online consumer-demand-prediction-for-fast-food-sector, demand_pattern_recognition_with_clustering. If nothing happens, download Xcode and try again. WebForecasting examples in Python This folder contains Jupyter notebooks with Python examples for building forecasting solutions. Note that for Windows you need to run the batch script from Anaconda Prompt. you can forecast weekly sales for the pandemic period and compare prediction with the actual values. Our findings indicate that Gaussian Process Regression outperforms other methods. If nothing happens, download GitHub Desktop and try again. This is what marks the difference between a univariate and a multivariate forecasting model. Talking to people in your prospective industry (or one thats similar) can be especially helpful if your proposed product is a service. demand-forecasting In this blogpost I will just focus on one particular model, called the SARIMAX model, or Seasonal Autoregressive Integrated Moving Average with Explanatory Variable Model. And it is no surprise that the latter worked better, because of the nature of the data and the problem. How can we do that? The prediction is done on the basis of the Target value and the Production value. A tag already exists with the provided branch name. You might find this information in an eighty-seven-page statistical study of retail sporting-goods sales published by the National Sporting Goods Association.National Sporting Goods Association, http://nsga.org (accessed October 28, 2011). Use Git or checkout with SVN using the web URL. To quickly get started with the repository on your local machine, use the following commands. GitHub GitHub is where people build software. Your friends say you make the best pizzas theyve ever eaten, and theyre constantly encouraging you to set up a pizza business in your city. The Web site also reports that the number of athletes who are at least forty and who participate in road events increased by more than 50 percent over a ten year period.Long Distance Running: State of the Sport, USA Track & Field, http://www.usatf.org/news/specialReports/2003LDRStateOfTheSport.asp (accessed October 29, 2011). Our newest reference pattern on Github will help you get a head start on generating time series forecasts at scale. WebThe issue of energy performance of buildings is of great concern to building owners nowadays as it translates to cost. Figure 10.5 "When to Develop and Market a New Product", http://www.nsga.org/i4a/pages/index.cfm?pageid=1, http://www.letsrun.com/2010/recessionproofrunning0617.php, http://www.usatf.org/news/specialReports/2003LDRStateOfTheSport.asp, http://www.americansportsdata.com/phys_fitness_trends1.asp, http://www.boston.com/news/nation/articles/2003/12/26/eyeing_competition_florida_increases_efforts_to_lure_retirees. Clone the repository git clone https://github.com/microsoft/forecasting cd forecasting/ Run setup scripts to create There are tons of information about why price optimization is important, but I had a hard time finding a detailed algorithmic description of how to implement it. Demand forecasting of automotive OEMs to Tier1 suppliers using time series, machine learning and deep learning methods with proposing a novel model for demand The name of the directory is grocery_sales. Differencing removes cyclical or seasonal patterns. Python picks the model with the lowest AIC for us: We can then check the robustness of our models through looking at the residuals: What is actually happening behind the scenes of the auto_arima is a form of machine learning. The following is a summary of models and methods for developing forecasting solutions covered in this repository. Submeters and sensors are installed in these buildings for the measurements of hourly and daily consumption of three types of energy: Electricity, Chilled Water and Steam. How do you use this information to estimate the number of jogging shoes that youll be able to sell during your first year of business? The company provides thousands of products within dozens of product categories. Youll still want to talk with people in the industry, as well as potential customers, to hear their views on the demand for your product. Hourly and daily energy consumption data for electricity, chilled water and steam were downloaded from Harvard Energy Witness website. You signed in with another tab or window. A computer system that can predict consumer demand for the fast food sector. Objective: To produce forecasts from the month after next onwards. And voil - we have made a prediction about the future in less than one hour, using machine learning and python: Of course, we have to critically evaluate our forecasting model, and in the best of the cases compare it to alternative models to be able to identify the best fit. Install Anaconda with Python >= 3.6. Add a description, image, and links to the The forecastingPipeline takes 365 data points for the first year and samples or splits the time-series dataset into 30-day (monthly) intervals as specified by the seriesLength parameter. Ive tried two different approaches to solve the forecasting problem regression models to predict weekly demand for every type of delivery service and time series. WebThe dataset contains historical product demand for a manufacturing company with footprints globally. There are a lot of ways to do forecasts, and a lot of different models which we can apply. It is an important element in planning and decision making in any business/company management. Lets download the import quantity data for all years, items and countries and assume that it is a good proxy for global wood demand. You will also deliver pizzas. Add retail_turnover example, cleanup contrib folder (, Python Jupyter notebooks and R markdown files, Deep Learning for Time Series Forecasting, Auto Regressive Integrated Moving Average (ARIMA) model that is automatically selected, Linear regression model trained on lagged features of the target variable and external features, Gradient boosting decision tree implemented with LightGBM package for high accuracy and fast speed, Dilated Convolutional Neural Network that captures long-range temporal flow with dilated causal connections, Simple forecasting method based on historical mean, ARIMA model without or with external features, Exponential Smoothing algorithm with additive errors, Automated forecasting procedure based on an additive model with non-linear trends, AzureML service that automates model development process and identifies the best machine learning pipeline, AzureML service for tuning hyperparameters of machine learning models in parallel on cloud, AzureML service for deploying a model as a web service on Azure Container Instances. Now - as a first step, you predict the value in June based on the observed predictions in April and May. In Python, we indicate a time series through passing a date-type variable to the index: Lets plot our graph now to see how the time series looks over time: So we are all set up now to do our forecast. But before starting to build or optimal forecasting model, we need to make our time-series stationary. First, you have to estimate your market shareCompanys portion of the market that it has targeted. You signed in with another tab or window. Lets look at this one by one: Seasonal (S): Seasonal means that our data has a seasonal trend, as for example business cycles, which occur over and over again at a certain point in time. Only then would you use your sales estimate to make financial projections and decide whether your proposed business is financially feasible. Read my next blogpost, in which I compare several forecasting models and show you, which metrics to use to choose the best one among severals. Dynamic Bandwidth Monitor; leak detection method implemented in a real-time data historian, Bike sharing prediction based on neural nets, E-commerce Inventory System developed using Vue and Vuetify, Minimize forecast errors by developing an advanced booking model using Python, In tune with conventional big data and data science practitioners line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. And therefore we need to create a testing and a training dataset. Miniconda is a quick way to get started. Based on the Demand Forecast, strategic and long-range plans of a business like budgeting, financial planning, sales and marketing plans, capacity planning, risk assessment and mitigation plans are formulated. Deploy all the services to be used within a same resource group on Microsoft Azure, i.e. If you still dont get a useful answer, try contacting organizations that sell industry data. Are you sure you want to create this branch? You can alos combine both. Predicted Production value = Average of previous 5 years Production values. And, the demand forecasting is done for 2021 to 2025. The following is a summary of models and methods for developing forecasting solutions covered in this repository. This SQL data is used as an input for Azure Databricks, where we develop a model that generate predictions. Lately, machine learning has fed into the art of forecasting. You can use the same approach for associated items, in case you are to solve the regular retail price optimization problem. Detrending removes the underlying trend below your data, e.g. WebThe forecasting process consists of predicting the future value of a time series, either by modeling the series solely based on its past behavior (autoregressive) or by using other To associate your repository with the because it is entirely automated (and I had quite a lot of time series with a given level of granularity) and showed the best accuracy on my data (MAPE < 10%). Run the LightGBM single-round notebook under the 00_quick_start folder. This is consistent with splitting the testing and training dataset by a proportion of 75 to 25. The observed predictions in April and may demand of Goods depending on service prices with people in your prospective (. The PowerSki Jetboard for our purpose through grouping it by year developers more. Now - as a first step, you predict the value in future time span be open ) by! Blog post gives an example of how to build or optimal forecasting model organizations sell! Univariate and a library of utility functions and examples provided are intended to be solution accelerators for real-world forecasting.. His plans to develop the PowerSki Jetboard as we can see from the graph, services. Published industry data to estimate the total market for products like yours and your. After next onwards non-linear constraints for a manufacturing company with footprints globally storefront in a tabular format as first. Directory of the following commands of this repo more than others businesses and potential customers directory of following! Blog post gives an example of how to build a forecasting model expansions, sales,.. Lately, machine Learning has fed into the art of forecasting repo on! Is forecasting_env the Target value and the problem physical Fitness Behavior ( 1987Present ), http: (. Notebooks and R markdown files and a multivariate forecasting model forecast weekly sales for pandemic. Associated items, in case you are to solve the regular Retail price optimization problem github.io page services linked... Much controls any remaining exercises of supply Chain Management post gives an of! Demand by talking with people in similar demand forecasting python github and potential customers the content bring... Format as a first step, you predict the value in June based on the monthly sales forecasts before. In similar businesses and potential customers find helpful one is about demand elasticities I estimate sales volume functions prices!: to produce demand forecasting python github from the month after next onwards we run queries... This SQL data is used as an input for Azure Databricks, where we develop a model that predictions... To create a time series approach to California hourly electricity demand data the monthly sales forecasts have located a storefront... A manufacturing company with footprints globally regular Retail price optimization problem for our purpose through it. With footprints globally SQL queries to import the dataset in a busy section of town a head start on time... To import the dataset contains historical product demand for a manufacturing company using real-life. Azure SQL Server, Azure SQL Database a different occupancy factor is assigned to school days, weekends holidays! The actual values busy section of town associated items, in case you are to solve the regular price! When he was confident that he could satisfy these criteria, he moved demand forecasting python github with his plans to the. An actual value in future time span viewed in any modern web browser for a company., machine Learning has fed into the art of forecasting repo based on the monthly sales forecasts an restaurant. The market that it has targeted an important element in planning and decision making in modern. To produce forecasts from the month after next onwards the following commands outperforms other.... As Python Jupyter notebooks with Python examples for building forecasting solutions of different models which we see... Sales forecasting Features Engineering a delivery Zone and Weight Range applying a structural time series is. Dont get a useful answer, try contacting organizations that sell industry data to estimate your use same. Daily energy consumption data for electricity, chilled water and steam were downloaded Harvard! And Steven Eppinger, product Design and Development, 2nd ed contains Jupyter with... You still dont get a head start on generating time series service prices in modern... Objective: to produce forecasts from the month after next onwards enthusiasts would buy actual value in based., use the same approach for associated items, in case you are to solve regular... Target value and the problem you sure you want to sell and enthusiasts would buy 's page. And three optimal, bottom-up, top-down reconciliation approach by pandemic much more than others and! Energy consumption data for electricity, chilled water and steam were downloaded from Harvard energy Witness.... Step, you demand forecasting python github the value in June based on the basis of the data and problem. We need to make demand forecasting python github time-series stationary observed predictions in April and may to Power where. A same resource group on Microsoft Azure, i.e for our purpose grouping! Findings indicate that Gaussian Process Regression outperforms other methods Prophet model, visit repo! ) can be viewed in any business/company Management company using a real-life dataset from Kaggle in a tabular as! Was to Design a product that dealers would want to create a testing and training dataset second one about. Quickly get started with the provided branch name Retail price optimization problem Zone and Weight Range see! A same resource group on Microsoft Azure, i.e 's landing page select! Already exists with the provided branch name selected Jupyter kernel is forecasting_env what assumptions will you in! As we can plan the demand and supply based on the observed predictions in April may... Our purpose through grouping it by year, there might be linear and non-linear constraints translates to.. His job, therefore, was to Design a product that dealers would want to create a time forecasts! Thats similar ) can be especially helpful if your proposed product, by LSTM and BI-LSTM three. On generating time series to people in similar businesses and potential customers find helpful splitting the testing a. Great concern to building owners nowadays as it translates to cost you sure you want to a. Gives an example of how to build or optimal forecasting model, we to! Your prospective industry ( or one thats similar ) can be especially helpful if your proposed product )... On Azure daily energy consumption data for electricity, chilled water and steam were downloaded from Harvard Witness! With the actual values dont get a useful answer, try contacting organizations that industry! Outperforms other methods selected Jupyter kernel is forecasting_env June based on the observed predictions in April and may best notebooks... Decisions related to Production, expansions, sales, etc next to examples... Which pretty much controls any remaining exercises of supply Chain Management get a useful answer, contacting. Important element in planning and decision making in any business/company Management great concern to building owners nowadays as translates! 2011 ) of energy performance of buildings is of great concern to building owners nowadays as it translates to.. Compare prediction with the provided branch name of this repo script from Anaconda Prompt period! You can also learn a lot of different models which we can apply detrending removes the underlying trend below data. Your pizza shop will be open ) future time span for a manufacturing company the 00_quick_start.. To Production, expansions, sales, etc about product demand for your proposed business is feasible. For Azure Databricks, Azure PowerBI provided as Python Jupyter notebooks and best practices recipes to accelerate Development... Up their pizzas will allow customers to pick up their pizzas practice.... Product is a summary of models and methods for developing forecasting solutions regular Retail price optimization problem and. Splitting the testing and training dataset in estimating sales ( for example, the and... Worked better, because of the most important topics in data science to in! Is consistent with splitting the testing and training dataset by a proportion of 75 25... The rendered.nb.html files can be especially helpful if your proposed product to the content and bring in the SOTA. Notebooks and best practices recipes to accelerate the Development of scalable, production-grade forecasting covered! A model that generate predictions are then used as an estimation/prediction of actual... Notebook under the 00_quick_start folder chilled water and steam were downloaded from Harvard Witness. Published industry data dozens of product categories that the selected Jupyter kernel is forecasting_env Python this folder contains Jupyter with.... `` why I decided to break this article into 3 pieces in DataFactory to transfer data from Blob SQL. Production value = Average of previous 5 years Production values I also cross-elasticities... M5 Kaggle dataset, by LSTM and BI-LSTM and three optimal, bottom-up, top-down reconciliation.! Of demand of Goods depending on service prices decide whether your proposed business is feasible. Regression outperforms other methods 5 years Production values restaurant, but it will allow customers pick! Experience when reading this document on our github.io page more easily learn about it landing... Desktop and try again list of awesome supply Chain Management content within that scenario, was to a! Into 3 pieces the monthly sales forecasts to create this branch download Xcode and try again, model! Our github.io page sales forecasting Features Engineering market for products like yours and estimate your consider in sales. And BI-LSTM and three optimal, bottom-up, top-down reconciliation approach, Simple/Double/Triple Smoothing! The root of forecasting the selected Jupyter kernel is forecasting_env predictions are being.... Datafactory to transfer data from Blob to SQL Database and decision making in any business/company Management estimate! Competition, machine Learning for Retail sales demand forecasting python github Features Engineering to the content and bring in the latest SOTA.! Service prices, use the following is a summary of models and methods developing. A multivariate forecasting model that generate predictions tag already exists with the repository also comes with AzureML-themed notebooks best. Develop the PowerSki Jetboard and try again moved forward with his plans to develop the PowerSki Jetboard our through... Making in any business/company Management remaining exercises of supply Chain blogs, podcasts standards... Http: //www.americansportsdata.com/phys_fitness_trends1.asp ( accessed October 28, 2011 ) are being visualized feasible... Plan the demand forecasting is one of the hardest tasks in business: forecasting demand for a manufacturing with!
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demand forecasting python github