forecasting | Time Series Forecasting Best Practices & Examples | Machine Learning library

 by   microsoft Python Version: v0.2.0 License: MIT

kandi X-RAY | forecasting Summary

kandi X-RAY | forecasting Summary

forecasting is a Python library typically used in Retail, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. forecasting has no bugs, it has no vulnerabilities, it has a Permissive License and it has high support. However forecasting build file is not available. You can download it from GitHub.

Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. 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. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featurizing the data, optimizing and evaluating models, and scaling up to the cloud. The examples and best practices are provided as Python Jupyter notebooks and R markdown files and a library of utility functions. We hope that these examples and utilities can significantly reduce the “time to market” by simplifying the experience from defining the business problem to the development of solutions by orders of magnitude. In addition, the example notebooks would serve as guidelines and showcase best practices and usage of the tools in a wide variety of languages.
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            kandi-support Support

              forecasting has a highly active ecosystem.
              It has 2548 star(s) with 419 fork(s). There are 104 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 0 open issues and 79 have been closed. On average issues are closed in 401 days. There are no pull requests.
              It has a positive sentiment in the developer community.
              The latest version of forecasting is v0.2.0

            kandi-Quality Quality

              forecasting has 0 bugs and 0 code smells.

            kandi-Security Security

              forecasting has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              forecasting code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              forecasting is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              forecasting releases are available to install and integrate.
              forecasting has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed forecasting and discovered the below as its top functions. This is intended to give you an instant insight into forecasting implemented functionality, and help decide if they suit your requirements.
            • Create features from pred_round
            • Returns the week of the given date_time
            • Generate a dataframe from a list of dictionaries
            • Compute moving averages for a moving window
            • Return a pandas dataframe with the given lag
            • Combine features
            • Define the training data schema
            • Specify data schema
            • Check if time format is correct
            • Check that the feature column is a static feature column
            • Check the frequency of a time series
            • Check that the column names in df_col_names are valid
            • Download OJdata files
            • Download a file
            • Returns the git repository path
            • Get or create a Azure workspace
            • Return an auth type
            • Return the week of the given date_time
            • Creates a dataframe from a cartesian product
            Get all kandi verified functions for this library.

            forecasting Key Features

            No Key Features are available at this moment for forecasting.

            forecasting Examples and Code Snippets

            Citywalks,Evaluating Multiple Object Forecasting models on Citywalks
            Pythondot img1Lines of Code : 2dot img1License : Permissive (MIT)
            copy iconCopy
            gdown https://drive.google.com/uc?id=1PXzIOHGScWqhAWXnO4q70fch8wF1DJUx
            
            python evaluate_outputs.py -gt ./outputs/ground_truth/ -pred ./outputs/sted/
              
            copy iconCopy
            ./run.sh
              
            Rolling/Time series forecasting-The rolling mechanism
            Jupyter Notebookdot img3Lines of Code : 0dot img3License : Permissive (MIT)
            copy iconCopy
            import pandas as pd
            df = pd.DataFrame({
               "id": [1, 1, 1, 1, 2, 2],
               "time": [1, 2, 3, 4, 8, 9],
               "x": [1, 2, 3, 4, 10, 11],
               "y": [5, 6, 7, 8, 12, 13],
            })
            from tsfresh.utilities.dataframe_functions import roll_time_series
            df_rolled = roll_tim  

            Community Discussions

            QUESTION

            Multi-Step Ahead Time Series Forecasting (Using Direct Approach)
            Asked 2022-Apr-05 at 01:12

            So I'm relatively new in R and I was wondering what's wrong with my loop forecasting multi-step time series.

            I first have this loop to mimic the information set at time τ and estimate the models based on a rolling window of 1000 observation and make a one-step-ahead out-of-sample forecast with 726 out-of-sample observations.

            ...

            ANSWER

            Answered 2022-Apr-05 at 01:12

            I finally found the solution by myself, here it is for those who will encounter this problem :

            Source https://stackoverflow.com/questions/71670920

            QUESTION

            What does a Keras TimeDistributed layer actually do?
            Asked 2022-Mar-30 at 16:16

            Given a time-series, I have a multi-step forecasting task, where I want to forecast the same number of times as time steps in a given sequence of the time-series. If I have the following model:

            ...

            ANSWER

            Answered 2022-Mar-30 at 16:16

            Based on the example you posted, the TimeDistributed will essentially apply a Dense layer with a softmax activation function to each timestep:

            Source https://stackoverflow.com/questions/71572843

            QUESTION

            Why SparseCategoricalCrossentropy is not working with this machine learning model?
            Asked 2022-Mar-29 at 07:03

            I have a .csv database file which looks like this:

            ...

            ANSWER

            Answered 2022-Feb-06 at 19:25

            Assuming the labels are integers, they have the wrong shape for SparseCategoricalCrossentropy. Check the docs. Try converting your y to one-hot encoded labels:

            Source https://stackoverflow.com/questions/71008696

            QUESTION

            ARIMA forecast model don't show on graph
            Asked 2022-Mar-28 at 11:23

            I'm currently using the ARIMA model to predict a stock price, SARIMAX(0,1,0). I wanted to forecast stock prices on the test dataset with 95% confidence interval. I'm following this tutorial posted July 2021, but some things have changed and I can't figure out what.

            The original code are as follows:

            ...

            ANSWER

            Answered 2022-Mar-28 at 11:23

            Note that to set the forecast indices and confidence intervals, we subtract 57 from the total number of elements. Data is also requested for the upper and lower confidence interval, for their subsequent drawing(conf_ins = fitted.get_forecast(57).summary_frame()).

            Source https://stackoverflow.com/questions/71642088

            QUESTION

            Best way to perform arbitrary operations on groups with Dask DataFrames
            Asked 2022-Mar-24 at 15:01

            I want to use Dask for operations of the form

            ...

            ANSWER

            Answered 2021-Dec-10 at 06:13

            This is speculative, but maybe one way to work around the scenario where partition-group leads to rows from a single group split across partitions is to explicitly re-partition the data in a way that ensures each group is associated with a unique partition.

            One way to achieve that is by creating an index that is the same as the group identifier column. This in general is not a cheap operation, but it can be helped by pre-processing the data in a way that the group identifier is already sorted.

            Source https://stackoverflow.com/questions/70265639

            QUESTION

            Feature importance or model summary in sktime
            Asked 2022-Mar-21 at 09:35

            I'm going through the documentation of the sktime package. One thing I just cannot find is the feature importance (that we'd get with sklearn models) or model summary (like the one we can obtain from statsmodels). Is it something that is just not implemented yet?

            It seems that this functionality is implemented for models like AutoETS or AutoARIMA.

            ...

            ANSWER

            Answered 2022-Mar-21 at 09:35

            Ok, I was able to solve it myself. I'm really glad the functionality is there!

            The source code for ForecastingPipeline indicates that an instance of this class has an attribute steps_ - it holds the fitted instance of the model in a pipeline.

            Source https://stackoverflow.com/questions/71545626

            QUESTION

            How do I calculate the ADI and COV of time series data using SQL?
            Asked 2022-Feb-10 at 05:03

            I'm trying to classify time series data using SQL. I have data for a reference data point that occurs over 3 years. So the reference occurs 36 times, one for each month. Sometimes the quantity is 0, other times it may be 25 or even higher for each row. What I want to know is how to calculate these equations using SQL (MSSQL in particular).

            Then, similarly, I want to classify the data into Erratic, Smooth, Lumpy, and/or Intermittent as seen here.

            Smooth demand (ADI < 1.32 and CV² < 0.49). The demand is very regular in time and in quantity. It is therefore easy to forecast and you won’t have trouble reaching a low forecasting error level.

            Intermittent demand (ADI >= 1.32 and CV² < 0.49). The demand history shows very little variation in demand quantity but a high variation in the interval between two demands. Though specific forecasting methods tackle intermittent demands, the forecast error margin is considerably higher.

            Erratic demand (ADI < 1.32 and CV² >= 0.49). The demand has regular occurrences in time with high quantity variations. Your forecast accuracy remains shaky.

            Lumpy demand (ADI >= 1.32 and CV² >= 0.49). The demand is characterized by a large variation in quantity and in time. It is actually impossible to produce a reliable forecast, no matter which forecasting tools you use. This particular type of demand pattern is unforecastable.

            Here is the query that produces the table that I am working with.

            ...

            ANSWER

            Answered 2022-Feb-09 at 22:00
            with data as (
                select
                    CP_REF,
                    count(*) * 1.0 /
                      nullif(count(case when QUANTITY > 0 then 1 end), 0) as ADI,
                      stdevp(QUANTITY) / nullif(avg(QUANTITY), 0) as COV
                from DF_ALL_DEMAND_BY_ROW_V
                where parent is not null
                group by CP_REF
            )
            select
                CP_REF, ADI, COV,
                case
                    when ADI <  1.32 and COV <  0.49 then 'Smooth'
                    when ADI >= 1.32 and COV <  0.49 then 'Intermittent'
                    when ADI <  1.32 and COV >= 0.49 then 'Erratic'
                    when ADI >= 1.32 and COV >= 0.49 then 'Lumpy'
                    else 'Smooth'
                end as DEMAND
            from data;
            

            Source https://stackoverflow.com/questions/71055377

            QUESTION

            Date format error while forecasting using tsibble objects
            Asked 2021-Dec-07 at 13:28

            I have converted a normal DF into a tsibble object and used that for my time-series forecasting. While fitting the model I experience the date format error- "Error in decimal_date.default(x) : date(s) not in POSIXt or Date format". As you could see from the below code- the converted tsibble object clearly identifies column "Week.1" as week date type. Could you please help me clarify why I'm still getting the date format when I fit forecast models to the tsibble object?

            ...

            ANSWER

            Answered 2021-Dec-07 at 13:28

            You are mixing 2 different ways of doing forecasts. you either use fable or you use forecast. auto.arima is from the forecast package. Though it does work with fable, it is better to keep everything to the same package eco system. Fable is the successor of forecast. Your library loading problably conflicted somewhere.

            For arima forecasts check out chapter 9.7 from Forecasting: Principles and Practice 3rd edition.

            I adjusted your code to work with fable. I have included 2 ways of doing this. My preference is the second one, because then you can see the difference in AICc values and see that they are very close to each other.

            Source https://stackoverflow.com/questions/70255879

            QUESTION

            Time series forecasting using Fable in R; determining most optimum combination of models for mixed model
            Asked 2021-Dec-07 at 11:04

            I am doing some time series forecasting analysis with the fable and fabletools package and I am interested in comparing the accuracy of individual models and also a mixed model (consisting of the individual models I am using).

            Here is some example code with a mock dataframe:-

            ...

            ANSWER

            Answered 2021-Dec-07 at 11:04

            A couple of things to consider:

            • While it's definitely desirable to quickly evaluate the performance of many combination models, it's pretty impractical. The best option would be to evaluate your models individually, and then create a more simple combination using, e.g. the 2 or 3 best ones
            • As an example, consider that you can actually have weighted combinations - e.g. 0.75 * ets + 0.25 * arima. The possibilities are now literally endless, so you start to see the limitations of the brute-force method (N.B. I don't think fable actually supports these kind of combinations yet though).

            That, said, here's one approach you could use to generate all the possible combinations. Note that this might take a prohibitively long time to run - but should give you what you're after.

            Source https://stackoverflow.com/questions/70183054

            QUESTION

            Forecasting Volatility by EGARCH(1,1) using `arch` Package
            Asked 2021-Nov-25 at 09:50
            Purpose

            I want to predict daily volatility by EGARCH(1,1) model using arch package.
            Interval of Prediction: 01-04-2015 to 12-06-2018 (mm-dd-yyyy format)

            hence i should grab data (for example) from 2013 till 2015 to fit EGARCH(1,1) model on it, and then predict daily volatility for 01-04-2015 to 12-06-2018

            Code

            so i tried to write it like this:

            ...

            ANSWER

            Answered 2021-Nov-25 at 09:50

            You only need to pass the reindex=False keyword and the memory requirement drops dramatically. You need a recent version of the arch package to use this feature which changes the output shape of the forecast to include only the forecast values, and so the alignment is different from the historical behavior.

            Source https://stackoverflow.com/questions/70077187

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install forecasting

            To quickly get started with the repository on your local machine, use the following commands. 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.
            Install Anaconda with Python >= 3.6. Miniconda is a quick way to get started.
            Clone the repository git clone https://github.com/microsoft/forecasting cd forecasting/
            Run setup scripts to create conda environment. Please execute one of the following commands from the root of Forecasting repo based on your operating system. Linux ./tools/environment_setup.sh Windows tools\environment_setup.bat Note that for Windows you need to run the batch script from Anaconda Prompt. The script creates a conda environment forecasting_env and installs the forecasting utility library fclib.
            Start the Jupyter notebook server jupyter notebook
            Run the LightGBM single-round notebook under the 00_quick_start folder. Make sure that the selected Jupyter kernel is forecasting_env.
            We assume you already have R installed on your machine. If not, simply follow the instructions on CRAN to download and install R. The recommended editor is RStudio, which supports interactive editing and previewing of R notebooks. However, you can use any editor or IDE that supports RMarkdown. In particular, Visual Studio Code with the R extension can be used to edit and render the notebook files. The rendered .nb.html files can be viewed in any modern web browser. 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. The Tidyverts framework is still under active development, so it's recommended that you update your packages regularly to get the latest bug fixes and features.

            Support

            We hope that the open source community would contribute to the content and bring in the latest SOTA algorithm. This project welcomes contributions and suggestions. Before contributing, please see our Contributing Guide.
            Find more information at:

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