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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Estimating sales for new products in e-commerce

I am trying to find an ML solution for the following problem. Objective: Given a list of products of Stores B, C, D … estimate the Order Value (1 year timeframe, let’s say) that each product would ...
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Understanding of value forecasting from time series

I aim to generate a value from multivariate time-series data. To be more clear, an example: ...
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Why does a linear regression in time series forecasting does not provide a line in predictions?

I'm reading the TensorFlow Time Series forecasting Tutorial 1 trying to perform my own time series prediction. However, specifically on single-shot models section for multiple time steps, the Linear ...
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When should I use neural networks?

I am struggling with this exercise. The objective is "to build a recommendation system that predicts the next video" viewed by a user, given the data provided. So, the dataset consists in ...
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How to train ARIMA model on multiple similar time series?

I am having 'business potential' values of 4000 cities (having generic names to ensure anonymity) for 72 months. The data for an individual city is just 72 months so I clustered the entire dataset ...
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Time Series Analysis for Categorical Data Output

Suppose I am having dataset which consist of date as one column and fruits as second column which is categorical data having set of 4 different fruits in that column and my output column has 0's and 1'...
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20 views

Produce a forecast based upon multiple time series with variable lag

Firstly, I'm not a data scientist, but I am keen to understand the power of the subject and have invested some time in learning - most examples of time series analysis, however, consider only a single ...
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Does this ARIMA model take seasonality into account?

I'm writing a tutorial on traditional time series forecasting models. One key issue with ARIMA models is that they cannot model seasonal data. So, I wanted to get some seasonal data and show that the ...
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First-differencing non-stationary time series when fitting neural networks

I am trying out different types of neural networks for time-series forecasting and I have not been able to find a satisfying answer online about whether or not non-stationary series should be first-...
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Time Series Forecasting for multiple value prediction for any date

In a given example like : Imagine there is historic data stored for a library, and you need to predict the number of books that the library will have, and the number of people who will borrow these ...
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How to make a forecasting model using labelled time series data of x to predict y?

I have made regression models in the past for this using x to predict the instantaneous value of y, however, I am curious if a time series approach could be more suitable. I'm working in python and I ...
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What is the best model for predicting delays?

Supposing we need to predict delays based on a previous dataset that contains the history of several, lets say, providers and their delivery delays. The goal is to minimize the loss due to those ...
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Time Series Forecasting for Yearly Data

I have a project that will be focused on collecting financial data from users (Revenues and Expenses). I want to include and AI solution that can take the data for each user and give them a ...
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How do I use number of hours as index in timeseries forecasting?

I have a dataset that has number of hours (consecutive value) and total sales in that 1 hour in my dataset. See below for head of the dataset: ...
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Test data for time sequential data

If I am trying to predict: the weather, the stock market, coffee sales per city, etc. there is no good way I can see to break out the data for training vs test data. For the weather case, training ...
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How can I train a LSTM with different time series of same process?

I have multiple time series dataset of the same process (e.g: sensor collecting humidity in a manufacturing process which last 2 hours) and would like to train a LSTM model to make forecast based on ...
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Forecast Model to Estimate Customer Service Call Volume and Appropriate Staff

I am working on a project to predict the proper staffing needed for a customer service team using historical data. I am new to machine learning, and I am not sure if my approach to this problem is the ...
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How to compare different forecasting models over different time horizon?

Developed multiple Models with AR, ARIMA, VAR; LSTM , SARIMA. Now, the purpose is to find out which model performs best on a given use case with different time horizons. The time series data is ...
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Time series Forecasting without consistent timestamps

I am currently working on a time series forecasting model with a dataset that does not have consistent timestamps i.e. one row every 60 seconds. Is it possible to train an accurate model with this ...
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What are some deep learning models use in timeseries forecasting that include context from covariates?

I was going through the literature for time-series forecasting using DL and all the methods I read about only use the variable of interest at previous timesteps to predict the same variable at time ...
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Grouped Time Series forecasting with scikit-hts

I am trying to forecast sales for multiple time series I took from kaggle's Store item demand forecasting challenge. It consists of a long format time series for 10 stores and 50 items resulting in ...
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How to use time series forecasts as input features?

I have a time series dataset containing daily data like below. Let's assume that I would like to make some forecasts of my temporal serie (x) and use it as a second feature feature (f) to predict the ...
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What are more advanced techniques than ARIMA?

For timeseries predication cases, what other techniques are available in statistics or machine/deep learning other than MA (moving average), ARMA, and ARIMA?
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What reccent alternatives to LSTM are there for regression problems?

I have been working for a while on a regression problem - predicting the air pollution in a city based on meteorological features (humidity, temperature, wind velocity a.o.). I have trained an LSTM ...
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Scaling multi input LSTM

I have a single layer LSTM model with 300 time series which try to predict the next value for one time series, based on past 12 values of the 300 time series. 56 is the number of slices of length 12 ...
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How to manage multiple timeseries model for large number of users?

I have to build timeseries forecasting for large number of users around 50K, what will be the ideal strategy to build forecasting model for such scenario where there are so many different users ?
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Managing Multiple Observation at the same time stamp timeseries forecasting deep learning

I have a dataset timeseries forecasting that includes the categorical columns and numeric as well. here is a sample of it ...
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Very low error during training of a RNN for forecasting but high test error

I use a Recurrent Neural Network for time series forecasting of electrical load data from a cooling device based on past values of the load time series and temperature values. I first normalize the ...
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ML algorithm for high dimensional time series forecasting

I'm trying to make a forecasting model for goods prices in an economy (trying to forecast inflation). Dataset: has 300 goods prices % monthly variations for last 6 years. And also added $n$ ...
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How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?

I have had a dataset below: sensor1 sensor2 sensor3 ... 2021-01-01 1.32 2.2 1.0 2021-01-02 4.3 2.0 0.8 ... ... I know ...
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Var_Imp Algorithms in Pred/Class Problems: Can I use it in TS Problems?

OBJECTIVE OF THIS POST: Solve a query about the possibility of use prediction/classification variable importance tools in a time series type dataframe. Collect the largest number of variable ...
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Prediction Intervals on (Multi-Step) Judgement Forecasts

Are there any R packages available or general methodologies for calculating prediction intervals on judgment-based forecasts? I've looked at Hyndman's text and the R forecast package - which will ...
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identifying time series with threshold breach potential

(moved from stackoverflow.com) Hi all, I'm trying to solve a following problem. I have a set of various devices feeding their readings into a system where they are stored as time series: timestamp, ...
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Forecast methodology for geographic variables that are somewhat related

I'm creating time series forecasts for different geographies and wanted an expert opinion on how I can take into account geographic relationship to improve my model. Is there an algorithm that's ...
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What could be reasons for higher MAPE?

I built two models on a dataset where data for independent variables (X) being the same and dependent variable (Y) changes for each model eg : Y is price value for a particular region. Y values change ...
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Getting KeyError while executing forcast( ) in statsmodel's holtwinters function

I'm trying to get time series prediction using the following code. ...
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How to deal with mismatch of timesteps in target variable and features in forecasting problem?

Background Info: I am working with some climate data where I want to predict crop yields with my dataset containing climate- and satellite-derived features. This is a time series regression ...
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Sarimax forecast : How to properly deal with non working days

(asked first on stackoverflow but felt like it would be smarter to put it here) I'm trying to build a Sarima model to predict day by day the expected value of several measures (separately), the point ...
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Panel data classification and regression?

I am very new to time series/panel/longituginal data. From my understanding Panel data = multi-object time series and I have some panel data in long format (objects have multiple rows corresponding to ...
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Choosing kernel size of cnn for time series data with multiple seasonalities

I try to solve a standard time series forecasting problem using convolutional neural networks. The data has multiple seasonalities and so I wonder if a kernel size should reflect this fact e.g. for a ...
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Optimise for the sum of regression predictions?

I'm building a machine learning model to forecast the number of students on a course at a University. I'm currently optimising for MAE for each sample (i.e. a ...
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CoxPH model with Frailty and L1 regularization

This question stems from an approach proposed by Dr. Silverman, "Predicting Horse Race winners through A Regularized Conditional Logistic Regression with Frailty." In this paper, he proposes ...
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Is it possible to pass multiple features at once to Croston method?

I want to implement the Croston method for intermittent demand. I have a data frame that has 10 features and all those have many zeros in them. I want to pass the entire data frame to the Croston ...
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Time series forecasting for vibration prediction on Industrial machine production?

I'm working on a machine learning project related to an industrial machine. The goal of the project is to build a model that would be able to predict the vibration of the machine while it's in ...
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Predicting Y Values Properly in a Regression Task using Scaled Values (Random Forest & MLP)

I have a supervised learning regression task: I am trying to forecast demand for a product based on sales in past years. Data description: Samples (rows) - Demand for a certain product (at a certain ...
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131 views

Time series forecasting in Python with 2 categorical variables

What approach is the best for a time series forecasting where you want to include 2 categorical variables in python? Im not finding any useful information that can help guide me with this; mainly ...
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Forecasting mon missing time timeseries data

I have time series data for minutes interval. But due to some noise i have to remove some rows from data. Now, I have data with some missing time stamp. What should i do for forecasting in this case?
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In Time Series forecast, should Scaling be done on both train and test features combined ( test is 1 new data point)?

Let say I have a Time series, I'm using sliding/expanding window method to split to train and test data: train would be all the data I have until day x and test is day x+1. To avoid Data leakage I'm ...
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Machine Learning Model for Time Series Forecasting

I am using Random Forest, SVM, and XGBoost models to nowcast/forecast an economic time series variable. However, I would like to extend these models to optimize/customize them for time series ...
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One Year Ahead Forecasting with Unevenly Spaced Time Series

I have many products in my warehouses which can be "demanded" any day by my different clients. I want to forecast how many of each item will be demanded for the whole next year. Naturally, ...

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