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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Forecasting Multiple (few hundreds) uni-variate time series with inflated zeros

Hello Practitioners, Being a newbie seeking help to gain experience in Data Science. Lets take a scenario where a big company wants to forecast its sales (a specific product) across different stores ...
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2answers
73 views

Detecting abundance of a certain periodic pattern in a time series?

I am really stumped at the moment about how to solve a particular problem. I have many time series like this: This represents the number of hours a person spends on a website each day throughout the ...
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30 views

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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0answers
134 views

Elman RNN with keras

I have to perform multi-step multivariate forecasting of time series, using keras. I found an example where LSTM is used. I could modify that example replacing LSTM with SimpleRNN. Now I would like to ...
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0answers
44 views

Time series forecasting. How use future values

I have a time series dataset containing hourly data from a few year, like below. Let's assume that I want to make prediction for the next 3 hours (2021-01-01 19:00, 2021-01-01 20:00, 2021-01-01 21:00)....
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0answers
22 views

LSTM variable period prediction

I'm trying to train a model to predict the final cost of a product being developed over a few months. I have historical data of similar products which will be used for training the model. Some of the ...
2
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1answer
127 views

1st order Taylor Series derivative calculation for autoregressive model

I wrote a blog post where I calculated the Taylor Series of an autoregressive function. It is not strictly the Taylor Series, but some variant (I guess). I'm mostly concerned about whether the ...
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2answers
297 views

What are some good methods to forecast future revenue on categorical and value based data?

I have monthly snapshots (3 years) of all the contract data. It includes following information: Contract status [Categorical]: Proposed, tracked, submitted, won, lost, etc Contract stages [...
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0answers
18 views

How to automate Seasonal Arima?

I am building Seasonal Arima for more than 10k products. In all the tutorials and blogs mentioned, I need to do the exploration to find the p,d,q values along with seasonality value using the ...
2
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1answer
39 views

Formulate multivariate multistep time series forcasting using traditional machine learning, NOT deep learning

How do you represent multivariate multistep data using traditional machine learning? I know this seems like a tailored problem for RNN/LSTM, but I am wondering what the alternative machine learning ...
2
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1answer
72 views

Forecast multiple unevenly spaced time series

I am building a time-series forecasting model to predict some patterns in climatological data. The dataset consists of many (2 mln) time series which look for example as: However the observations ...
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0answers
172 views

ARIMA: How to understand performance of the model?

I am new to use of ARIMA model and after working on it for a couple of days and doing research - I'm not sure how to interpret the performance of my model... Here is what the ...
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0answers
78 views

Problem with Prophet Model regressior

I am working on predicting half-hourly UK electricity prices with prophet. I have two other time series: gas prices and initial national demand out-turn. So, after merging all the data-sets together ...
2
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1answer
83 views

How to combine data having similar distribution?

I have a collection of time series data with data points of around 2 years of daily data. I am thinking of a way to increase the number of data points in it so that the neural network gets a better ...
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0answers
39 views

Time Series and Statistical Learning methods differences

I am trying to understand the Time Series and Forecasting methods. I have this basic theoretical question about this: Why "Time Series" is NOT included on the Contents of, for example, the book "...
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0answers
31 views

Why might an LSTM be capable of predicting an ARMA signal but not a linear combination of ARMA signals?

I have an LSTM network and am testing it on some dummy ARMA signals. I'm trying to predict the signal 5 time steps into the future. The network is capable of outperforming Naive (persistence) when ...
2
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1answer
408 views

How to split a dataset into train and test sets for time series (multiple step-multiple output forecasting)?

I am trying to use an LSTM neural net to do multiple step / multiple output forecasting (I predict multiple values in one time knowing some values in the past). But, I have realized that I must be ...
2
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1answer
65 views

Building a time-series demand forecasting model

I am forecasting demand for certain types of goods and services, which I expect to be correlated to a sub-set of a basket of macroeconomic indicators (considering 15-20 indicators) I do not know ...
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0answers
335 views

ARIMA forecast for timeseries is one step ahead

I'm trying to forecast timeseries with ARIMA. As you can see from the plot, the forecast is one step ahead of the expected values. I read in some other threads that this behavior is expected but how? ...
2
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1answer
143 views

Training data : forecasted or actual?

I am working on a time series prediction problem. I am using keras models for machine learning. For this prediction, weather variables are used as input. They can be of two types: forecasted and ...
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1answer
62 views

Can I forecast with discontinued data using ARIMA?

I have data for sales on monthly basis, but a few months' information is not in the CSV file or data file. Can I forecast or fill that missing month with other calculated values from present records? ...
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0answers
2k views

Understanding how to use ConvLSTM for multistep ahead forecasting

I have a problem where I have transaction data for many banking accounts. The task is to train a model on historical debit/expense transactions and then forecast expense transactions for the next n ...
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940 views

Recommended model for univariate or multivariate multistep ahead time series forecasting

I have a dataset consisting of recurring and non-recurring expense transactions from bank accounts, as well as other features describing the bank account and each transation. I aggregate these ...
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1answer
124 views

How do I use rnn to forecast to n periods with limited data?

So this is my 1st time trying to run a small time-series dataset through an RNN, but after a lot of searching, I haven't been able to find, 1. How I can use this to forecast to n periods ? (like in ...
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0answers
5k views

Error when using seasonal arima in python

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1answer
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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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18 views

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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30 views

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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0answers
16 views

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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0answers
15 views

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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54 views

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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33 views

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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1answer
86 views

Predicting high frequency sparse time series data in python

I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0's, since there is no continuous flow of cars coming to charge but rather ...
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1answer
34 views

Predicting sparse time series data

I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0s, since there is no continuous flow of cars coming to charge, but rather ...
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0answers
26 views

Demand forecasting with marketing budget data

I'm trying to build a demand forecasting model to predict future daily orders of an online food takeout service (similar to UberEats or DoorDash). My first model uses a univariate approach, which is ...
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2answers
333 views

Train MLP Neural Network on time series data?

Newbie question here but I was curious to ask if an MLP Neural type network can be trained on time series data? The dataset that I have is an electricity type data set from a building power meter and ...
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1answer
20 views

Time series forecasting with constraints

I want to predict the passenger flow volume of an airline route, which subjects to supply capacity constraints of the route (i.e., the passenger flow volume should not be higher than the supply ...
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0answers
39 views

Machine Learning algorithms and Panel data

I have a large panel dataset composed of $N$ stocks, $T$ quarterly dates and $K$ features for each stock. The dataset looks like the following: ...
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1answer
32 views

LSTM model bad forecasts

I tried to implement LSTM model for time-series prediction. Below is my trial code. This code runs without error. ...
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22 views

Impact of covid19 in forecasting models

I have sales training data from 2019-06 to 2020-06 and I have to predict sales from 2020-06 ...
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225 views

Time series forecasting - Multiple Parallel Input and Multi-step Output

I have a time series with 5 variables, and I want to predict the behavior of these 5 variables 112 periods ahead. For this, I use a dataset with information on the 21504 periods before (18278 periods ...
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0answers
31 views

How to model a conditional demand forecast model as ex-ante forecast for a moving population?

Goal: I am trying to forecast demand from a specific population given a specific promotion on a certain period of time frame. The data is of the format: date | promotion % | sales in 1000s(y) 01-Jan |...
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0answers
31 views

How to handle large systematic missing data in time series?

I have this time series, where on the weekends, the dependent variable values are missing. It's only a time series, I do not have any exogenous regressors/features. The dependent variable value is an ...
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0answers
98 views

Getting vague results using VAR time series forecasting in python!

Firstly, I am a beginner in this field of Data Science and have tried to implement some time series models for wind speed forecasting. Also, I am aware of the fact that some regression models might ...
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1answer
32 views

How to predict orders with a range of items? And total orders which sum up to the total?

So I do have data like this: With the help of distinct order IDs, I can figure out how many orders are there and from units shipped, I can get the number of items in the order. Now I want to predict ...
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0answers
50 views

Web scraping using Beatiful Soup

I have this code and I wanna extract holidays, petrol and temperature but I don't know where is the problem. I need your help as soon as possible, please. I want to add this extraction to my dataset ...
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0answers
96 views

Why are Neural Network predictions "correct", but offset from true value? Not using any past lagged values

I recently asked a similar question, but didn't get a response that really addressed/fixed the issue. Additionally, I've done some more work since then. I'm sorry for the long question below, I just ...
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32 views

Time Series Analysis / Modeling with auto_arima

I recently dived into Time Series and was attempting at doing some data analysis and modeling. I'm using the following dataframe ...
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0answers
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Ljung-box test on weekly percentage of total quarter bookings

I have a data on the weekly percentage of the total quarter bookings. The data looks as follows (note: weekly percentages add up to 100 for each quarter) : (not real data) I used the Ljung-box test ...
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1answer
127 views

Building Timeseries models for stock trading having multiple stocks

I have gone through some of the tutorials on the timeseries and all of them have taken one stock for the timeseries and tried to forecast it. My dataset contains many stocks for the time period(each ...