forecasting | Time Series Forecasting Best Practices & Examples | Machine Learning library
kandi X-RAY | forecasting Summary
kandi X-RAY | forecasting Summary
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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Top functions reviewed by kandi - BETA
- 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
forecasting Key Features
forecasting Examples and Code Snippets
Lines of Code : 2License : Permissive (MIT)
Copygdown https://drive.google.com/uc?id=1PXzIOHGScWqhAWXnO4q70fch8wF1DJUx
python evaluate_outputs.py -gt ./outputs/ground_truth/ -pred ./outputs/sted/
License : No LicenseCopy./run.sh
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
Trending Discussions on forecasting
QUESTION
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 01:12I finally found the solution by myself, here it is for those who will encounter this problem :
QUESTION
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 16:16Based on the example you posted, the TimeDistributed will essentially apply a Dense layer with a softmax activation function to each timestep:
QUESTION
I have a .csv database file which looks like this:
...ANSWER
Answered 2022-Feb-06 at 19:25Assuming the labels are integers, they have the wrong shape for SparseCategoricalCrossentropy. Check the docs.
Try converting your y to one-hot encoded labels:
QUESTION
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 11:23Note 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()).
QUESTION
I want to use Dask for operations of the form
...ANSWER
Answered 2021-Dec-10 at 06:13This 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.
QUESTION
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 09:35Ok, 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.
QUESTION
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 22:00with 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;
QUESTION
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 13:28You 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.
QUESTION
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 11:04A 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 thinkfableactually 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.
QUESTION
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
so i tried to write it like this:
...ANSWER
Answered 2021-Nov-25 at 09:50You 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.
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Install forecasting
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.
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