Questions tagged [forecasting]
Forecasting is the process predicting future values based on historic and current data, typically for time-series datasets.
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Lack of Variability in Predictions from Multivariate LSTM Model
I've been working on a multivariate LSTM model for time series forecasting, but I'm encountering an issue where the predicted output doesn't exhibit enough variability. The predictions tend to be too ...
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Is it possible to use leftovers data (warehouse stocks data) to create sales forecasting?
For example, i have sales data by categories
| Date | GE | VIC |
| -- | -- | --|
|03.01.2022 |2|7|
|10.01.2022 |30 |12|
|17.01.2022 |15 |5|
|24.01.2022 |57 |8|
|.....|...|...|
|28.08.2023 |16 |2|
And ...
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Winter Holt's Time series model
I am confused with the Winter Holt's Time series model usage. I use 2 years of data to train and want to predict 3rd-year data.
Note1: I have partial 3rd year's data, but I want to use it to check my ...
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Can lag features be applied into test data without label?
can lag features be applied into test data without label? I've been wondering. I tried to build random forest model using dataset: training data (with label Y) and testing data (without label Y). The ...
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Stable test in online time series forecasting problem
I have a Time Series Forecasting problem. You can think of it as predicting the daily closing prices of Apple stocks. My data is divided into 4-day segments, and the forecasting is based on predicting ...
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How do we modify the early stopping procedure to account for better losses after initial rise in losses?
I have a question regarding the usage of early stopping in the training of my forecasting model. Curious about how the training would go without early stopping, I observed that the test loss seems to ...
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Can a multivariate MIMO LSTM forecaster learn the relationships between the multiple feature outputs?
Question: Can a multivariate MIMO LSTM learn the relationships between the multiple feature outputs?
This question arose when I decided to modify a multivariate (Multiple Input - Single Output, MISO) ...
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Predicting quanting sold using Time series data
I am struggling with a time series dataset comprising 12 features, including quantity sold and weather data, totaling approximately 1800 values. My goal has been to forecast future values, quantity ...
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Calculate the curve of a list and apply that to calculate the values of a larger list
I have a list of ~125 sorted ascending values and I am converting those to percentages of total so that they add up to 100% (shortened example would be: 0%, 0%, 15%, 15%, 30%, 40% with the total of ...
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How to assign sample weight for regression problem
I'm trying to model a forecasting problem where I'm trying to forecast for the following month. I am using LightGBM Regressor class for it and it giving me a decent ...
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Is timeseries forecasting for the next timeslot with a RNN a "Many-To-One" type application?
you often find applications that divide RNN according to their input and output data into the categories:
One-To-One
One-To-Many
Many-To-One
Many-To-Many
as you can see e.g. (here https://...
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Forecasting using LSTM Model
I have a dataset with 12 variables (x1, x2, ..., x12) and one target variable (y). Is it possible to perform a forecasting for the target variable (y) over a certain period of time ahead without ...
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Prediction intervals for future timestamps - out-of-sample
I've created a model for out-of-sample forecasting that uses multistep recursive strategy to reduce my problem to regression, the predictions are sufficient but I was wondering if there is any ...
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Good candidate time series models for 70-100 monthly data points, also incorporate past and future exogenous covariates
Per title, we're trying to identify a good time series modeling technique for:
70-100 variables of monthly sales or volume data (2015 or 2018 to present)
Ability to forecast not only using trend and ...
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Forecasting sequence data with intermittent peaks
I'm trying to forecast a sequence that looks like below:
I know ARIMA, INAR, GLM, etc. but none of these works for this data. Algorithms I found for intermittent time series (ADIDA, Croston, etc.) ...
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Growth (sales) forecast based on season
How to make a forecast based on last year (season)?
I have no experience in the field
Make a forecast of the sales in september 2023 based on last years sales
Example A
Sales august 2022 (last year): ...
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How to predict in a forecasting model when the data after training and prediction is missing?
Let's say we have a forecasting model that was trained on any data before 2021 and now we need to make a prediction on data in 2023, for an accurate prediction we need to either give the data of 2022 ...
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how to increase the accuracy of Prophet model?
I have a dataset representing the number of tickets over two years. I have used the prophrt model to predict make a prediction but the results don't seem to be accurate.
The code for the model is as ...
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Secondary model to correct RNN-based photovoltaic power generation forecast: Useful and what model type?
I am currently working on forecasting short horizon photovoltaic (PV) power generation. My primary forecasting model is an RNN which aims to forecast the PV power generation from the current day until ...
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Why does Time series split cause data leakage from future data?
There are lots of websites saying time series split may cause data leakage.
The idea for time series splits is to divide the training set into two folds at each iteration on condition that the ...
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LSTM multivariate forecasting
I'm currently working on timeseries forecasting in pairs where one timeseries is suspected to cause the other timeseries. I also have the forecast of the causing timeseries and I use them to predict ...
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Inverting the scaling of LSTM forecasts (tanh activation function)
I am working in R and the monthly time series of the M3 competition data (with values mostly around the 1000s).
I want to scale the series from -1 to 1 in order to satisfy the tanh activation function ...
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urgent!! i want to forecast health expenditure but the data is very small, only have a dataset of 20 samples
i tried using ARIMA but cannot due to small dataset. anybody know other time series forecasting for small dataset of 20 sample? urgent need help
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Can I train an LSTM on two features to predict a third?
I have a multivariate time series with three features x1,x2,x3, I have chosen window size 5 to make a prediction on variable x3 at time t=6. Is it correct to use only the LSTM input on the variables ...
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Trying to capture global time dependences for prediction with few time steps
I am trying to figure out the best approach for my prediction task. I have a dataset with four variables: year ranging from 2010 to 2022, categorical variables $A$ and $B$, and numeric target variable ...
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How to deal with systemic gaps in timeseries data
To be clear this question is not how to input missing data, but how to treat an exchange dataset that will not ever have data on weekends and occasionally on market holidays. I'm working with the ...
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what is the error here ? time series sliding windows
Given a list where each element is a dataframe, i want to create sliding windows in order to train a lstm model, but the problem is an error occurs.
Each dataframe is a time series with the 4 columns ...
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How to re-translate a forecast I wrote in R run on a dataset I downloaded from Tableau back into a SCRIPT_REAL function in Tableau
I want to add a SARIMA forecast of the next two days onto each line in the following Tableau graph:
But Tableau only does Exponential Smoothing forecasts and if I create a moving average table ...
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Growth trend in time series
I'm currently working with a dataset that looks as follows:
I'm using the prophet model provided by meta to forecast the data and I'm quite unsure about the parameter called 'growth'. Now there are $...
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What is the difference between lookback period and transform a time series dataset into a supervised learning dataset for time-series forecasting?
Let's say I have dataset within the following pandas dataframe format with a non-standard timestamp column without datetime format as follows:
...
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What is the best\correct data split approach over time-series data to compare performance of forecasting future data among ML and DL regressors?
Let's say I have dataset contains a timestamp (non-standard timestamp column without datetime format) as a single feature and count as Label/target to predict ...
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I want to forecast a NetCDF data
I have a chlorophyl-a and sea surface temp (SST) in a NetCDF format, the data is 4 dimensional mattrix, each data is saved inside certain longitude and lattitude value, if the data plotted into ...
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Time-series LSTM using current/past exogenous variables and past outcomes
I want to predict a time-dependent outcome (y) using current/past features (exogenous variables x) and past outcomes (y). The features also change with time.
In other words, for each sample (different ...
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State-of-the-art LSTM solutions to known datasets
I've seen that many ML datasets have competitions (like imageNet).
I've been looking for some kind of competition or state-of-the-art LSTM solutions for The Airline Passengers dataset but all I can ...
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lstm multi-step
i have a question about multi step forecasting result. for example, i have a dataset from 1 to 100. I applied sliding window technique and i used 24 timesteps to predict 20 future data. i had ...
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Best ML models for long term time series forecast
I have a project to make a long term prediction (like 5 years) of electricity production by types of power plants (solor, wind, coal, nuclear etc.).
I have access to time series data in MW [megawatts] ...
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Bugs in the backpropagation algorithm in Python
I've been trying to create a simple Neural Network from scratch with a backpropagation algorithm to predict the next number based on 3 previous numbers. But for some reasons, MSE(Mean Squared Error) ...
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How to forecast a timeseries with geolocation data?
I have created a dataset with my geolocations from the last three months. The data set contains longitude, latitude, and timestamp, with a frequency of every 5 minutes. Based on this data, I want to ...
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Problem formulation of future timeframe prediction based on current time
I have a problem where I want to predict "when is the next action happening" based on the time.
Example problem: Imagine you have a dataset of transactions per user, your goal is to predict ...
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Multivariate time series - predicting value on multiple correlated variables
I have a dataset of the following structure:
daily sales data for the last 5 years
monthly economic trends (there is actually more)
The objective is to forecast sales on daily & monthly level ...
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Treat multiple periods of huge outliers in time series data with weekly seasonality data
How can I model a time series data (average sales is around 20K) with weekly seasonality that has multiple recurring outlier periods for example 4 days of huge volumes (around 150K) in March and 14 ...
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time series forecasting with two columns
I have a task which is time series forecasting with two columns
to predict Number_of column. so I wonder what is the approach to deal with these two time series to predict Number_of column.
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Can Pybats' Analysis function make a prediction on a future DateTime object that is only one step beyond the final point of the existing data?
I was able to utilize the Bayesian approach of statistics in Pybats in order to make a forecast model on a timeseries dataset. While the model is learning from the ...
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Theory behind time Series Test dataset being the last x%
The standard flow for time-series that i'm aware of, is that you divide your dataset for Training & Validation (60% and 20% respectively for example) and the last 20% is used for Unbiased Testing....
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Should we reinsert trend after doing forecast using detrended data?
Basically, when we detrend a signal, we detect and remove a linear component of that signal. This produces a stationary version of that signal. And we can use various forecasting algorithms to ...
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How to do forecasting with categorical timeseries?
I have a dataset that is in the form of categorical timeseries: (specifically, we either know or don't know the values of 6 degrees of freedom of an object at any given time). If we know it, it's ...
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References to online service/platform dedicated to "time series prediction"
Do you have any references to online service/platform dedicated to "time series prediction" ?
For one product under development, we need to perform frequent (i.e. one per 15 minutes) ...
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What are some good methods for evaluating the disaggregation ratios in a top down approach?
Say we are forecasting at a high level (Department - Week) and we want to break it down to (Category - Week) level.
I want to find out which department's disaggregation ratios needs to be improved.
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Building recursive forecasting from scratch?
I am currently testing time-series modelling with tree-based (xgb, lgbm, cat) algorithms using, recursive lags of a y (sales) along with with numerous exogenous regressors i.e. date-time, price, ...
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