Questions tagged [forecasting]

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Forecasting - 3 independent variables

I have 3 independent input variables: time, temperature and pressure, and one output - usefulness. Example: Number of the sample; Time; Temperature; Pressure; Usefulness 1;1;30;50;9% 2;3;50;50;80% ...
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20 views

How to do Goal Based Time Forecasting?

I have a yearly budget of $10,000 and I have my past monthly expenditure data. Using the past monthly expenditure data, I need to predict the date at which I'm most likely to exhaust my yearly budget. ...
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1answer
42 views

LSTM with input of actual time step

I'm working on an implementation of LSTM neural network to forecast energy consumption. I have a dataset with load, series of weather parameters and indicator of it's bank holiday or not. I first ...
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1answer
46 views

Time Series Forecasting

I work in the Oil & Gas industry. I have been trying to build a ts forecasting model with covariates, and the model R code is as follows: ...
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16 views

Vector Autoregression forecasting with large dataset

I am trying to use VAR to forecast electricity price for a whole day and I have a dataset with over 20000 observations (price for every hour) from 2015-2017. My first intuition was to select 19975 ...
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1answer
140 views

LSTM regression bias increases when targets go close to 0

I've build a LSTM model for time series forecasting. Results are not bad, with a mean normalized error of 7%. However, this normalized bias shows a clear pattern: The closer to 0 the value to predict, ...
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0answers
56 views

Finding p values and estimates of external variables in facebook Prophet for forecasting in Python

I am using facebook Prophet for multivariate forecasting which has an objecting of forecasting prices. My target variable is affected by n number of external variables too. I am using add_regressor ...
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1answer
52 views

Need recommandations of using timing variables for forecasting sales

I have a data.table that contains many timing variables. Date: it gives the date of Sales Promo2(week, year): It describes the calendar week and the year when the store has started to participate in ...
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1answer
32 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
35 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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13 views

Does the R hts-Package work with time series of different units?

currently I'm having a look at the wonderful hts-Package. Its idea is to forecast hierarchical time series (like described here). In this example the unit of measurement across all time series is the ...
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1answer
99 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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22 views

Transformer Decoding in Inference mode for Time Series

With the Transformer model from "Attention is all you need" you have to feed in the the actual target during training. However, this can obviously not be done for actual inference. Now usually for ...
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2answers
331 views

Time series forecasting dilemma. Could feature engineering overcome time dependency?

I keep reading articles about time series forecasting. They all start from the same assumption: time series forecasting can't be treated as a regression/classification problem. It is time dependent, ...
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2answers
968 views

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 ...
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1answer
349 views

How to calculate customer purchase interval and predict next purchase in python?

Suppose we have a data set consists of columns TransactionId, CardNo, TransactionDate then how can we calculate the customer purchase interval (means if customer A purchased on Jan 1st and after ...
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0answers
16 views

How to deal with a particular month being under forecasted by the model?

I have 6 cycles of historical time series data of retail sales and using a prophet model to forecast one cycle. I am using 4 variables as regressors. There is one month in particular - 'April', which ...
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1answer
72 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 ...
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3answers
51 views

What is the minimum requirement for the dataset for time series forecasting?

I have a dataset of patients where, for each patient, a measurement is taken 3 times per day. For example, patient 1 has recordings at 7.30 am, 12.30 pm and 8.30 pm. Patient 1 has a collection of 30 ...
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1answer
49 views

How could I improve my FB Prophet forecast?

I've got 1325 days of revenue data and when plotting the components it makes 100% sense from a domain expert point of view, so the model is capturing the variations quite well (or it seems it does...)....
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1answer
21 views

How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?

I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
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1answer
40 views

Finding Outliers in Resource Forecast Data

Unsure if this is the correct place to place this, please close if so. I'm a workforce analyst at a large retail company, I own and maintain all the forecasting for our retail stores. This is based ...
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1answer
30 views

Forecasting Consumption for Multiple Products for Multiple Regions

Came across a very interesting Real-World Time Series Forecast Problem. Can you please help me understand the right track to resolve the below Time Series problem: Input Data Sample: and we want to ...
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30 views

Time series forecasting produce same values with different training data

I'm developing a python program which predict daily timeseries values. Each daily timeseries contains 288 values (a record every 5 minutes). The main idea is to train a LSTM model with 7 days data ...
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0answers
26 views

Linear correlation and XGboost regression for time series

I am working with sales time-series data, I have a history of 9 years of monthly data. I am trying to forecast sales for the next 12 months. I am using XGboost regression to build multivariate time ...
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1answer
84 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
2k views

Is there an R tutorial of using LSTM for multivariate time series forecasting?

There is a great blog post about how to use keras stateful LSTM in R to forecast sunspots. I applied it to financial ts data ...
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1answer
82 views

Is an Arma model equivalent to a 1-layer Recurrent Neural Network without activation function?

Given a time series $f(t)$ to forecast, let us consider an Arma model of the form: $$ f(t) = c + \sum_{i=1}^p a_i f(t-i) + e(t) + \sum_{j=1}^q b_j e(t-j) $$ where $e(t)$ are the forecast errors. On ...
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69 views

Does the add_regressor method on Facebook Prophet also work with categorical variables?

I went through the documentation of Facebook Prophet and was able to build a similar model for my time series dataset. The additional regressors I used were numeric. I achieved a reasonable MAPE score....
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1answer
202 views

Forecasting multiple time series with a single model

I have a dataset with sales numbers for ~500 different markets (assume different cities or regions) and need monthly forecasts for each market. Instead of building 500 different models, I'm interested ...
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1answer
35 views

How to forecast time series analysis for more then one dependent variables?

I have three datasets: Dataset_1= column name as ID, Date, counter_id Dataset_2= column name as ID, no. of tickets sold ...
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2answers
20 views

Convey time lag information to a linear regression model

I am using a simple linear regression to predict the number of units an item has moved and price of the item is one of the input parameters. For a few items, the older prices are not relevant and ...
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1answer
82 views

How is PACF analysis output related to LSTM?

I was going through a recent paper “A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature ForecastingUsing Long Short-Term Memory Neural Network Based on Ensemble Empirical ...
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2answers
993 views

LSTM future steps prediction with shifted y_train relatively to X_train

I'm trying to predict simple one feature time series data with shifted train data. The source looks like this: ...
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2answers
54 views

How to normalize a data set of multiple time series?

I have the a data set representing the electricity consumption of 25 000 customer. The electricity readings are taken from each smart meter each 15 min for a period of 3 days. The data is takes from ...
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1answer
268 views

Forecasting via LSTM or XGBoost… is it really a forecast or

I guess I understand the idea of predictions made via LSTM or XGBoost models, but want to reach out to the community to confirm my thoughts. This tutorial does a nice job explaining step by step of ...
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1answer
41 views

Forecasting time series outside the training/test set

I am trying to predict some time series based on precedent values using LSTM. I have pretty good results when I compare the predicted time series with the test set (0,18% error) I just miss how to ...
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0answers
11 views

What are good public datasets for time series analysis with “certified” (by papers in the literature) good predictors of the target variable?

I have to test different models for time series forecasting and predictors (exogenous covariates) goodness evaluation and I would like to use datasets used in relevant scientific publications that ...
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1answer
37 views

How to find feature importance with multiple XGBoost models

My problem statement : Time Series forecasting(Month wise data), training on 96 months of data and predicting next 12 months with a 3 months empty window in between. Example : Batch 1 ...
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30 views

How to understand logistic trend parameters proposed by Prophet (Facebook)?

I read the paper and I saw that the logistic trend is defined as below : $$ g(t) = \frac{C(t)}{1 + \exp{ -k(t) (t - m(t)) }} $$ Where $k$ and $m$ are respectively a growth rate and an offset parameter....
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52 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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27 views

LSTM forcasting: time series as input and a unique value for each as output

I am sorry for the mistakes I will make in this post, I'm new to deep learning ^^' I am trying to build an LSTM model that can help me predict some unique value according to time series indices and ...
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0answers
36 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 ...
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9 views

Context Dataset in Time Series Forecasting

In time series forecasting, what is context dataset? How different is it from the actual training dataset and what role does it play either in training or validation?
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1answer
27 views

forecasting - likelihood of customers participating in next month sales

I have historical transaction information of customers for the last 2 years and other information about the customers like what type of card (gold/platinum) they used for transactions etc. is also ...
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0answers
28 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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10 views

Cross-validation for Timeseries Counterfactual Analysis

We are looking to predict counterfactual states from time-series data. In our problem we are looking to determine the energy savings from a grid-installed device that is varied on and off for many ...
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0answers
21 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 ...
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13 views

For time series forecasting task, should I use data across several time steps or singe timing data for prediction?

I have a time series forecasting project, there are over 10, 000 time steps of data, so the data amount is not a problem. At first, I thought I've to create a time-based data pipeline that forms the ...