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Questions tagged [forecasting]

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0
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1answer
17 views

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

I have three datasets as: Dataset_1= column name as ID, Date, counter_id Dataset_2= column name as ID, no. of tickets sold ...
0
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1answer
40 views

Training a LSTM on a time serie containing multiple inputs for each timestep

I am trying to train a LSTM in order to use it for forecasting : the problem is basically a multivariate multi-steps time series problem. It is simply an experiment to see how statistical models (...
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0answers
28 views

Time-series forecasting

Here is the data: ...
1
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0answers
52 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 ...
1
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0answers
16 views

What's the difference between ELM and NNAR?

I'm working with time series forecasting using the two techniques that involve neural networks, the Extreme Learning Machine and the Autoregressive Neural Network. Reading the two methodologies, the ...
1
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0answers
21 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? ...
1
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0answers
53 views

How to model non-linear demand function?

I am trying to build a dynamic pricing algorithm on intermittent data (a lot of zeros between non-zero values). I have on average 100 non-zero data points for each product. However, it seems to be ...
0
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1answer
65 views

What Predictions can be done for Restroom/Washroom Data? [closed]

My company has installed sensors, which actually monitors the Restrooms/Washrooms in the Building. Currently the sensors collect data such as Ammonia, Nitrous and Visitors Count; All the data ...
0
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0answers
8 views

How can I enrich train data in case of cnn using target and time features

I have a sequence of images, let's say we ignore time specificity for now. In the other hand, target is a multivariate continuous time series. Let's consider it just a univariate one. Training a cnn ...
1
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0answers
27 views

LSTM - Forecasting usage (real world) [closed]

I see that LSTM is very powerful reconstructing time series it was fed with, but my issue is: => Can LSTM predict future values without requiring real (training/testing) data? My objective is simple:...
1
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0answers
22 views

forecast product demand in one week using machine learning approach

I'm trying to predict product demand in store. The predictors I have include price, competitor's price, store ID, date. My target variable is sales volume (in a particular store). What I need to ...
2
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1answer
46 views

how to predict content based demand

this is my first post at ds StackExchange, so please be gentle and let me know if something is not clear :) I have many products (>1M), and I save all the products purchases in a DB with a time stamp....
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0answers
223 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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0answers
8 views

Long run time for grid search SARIMA

I am running a grid search for identifying the right set of params for Seasonal ARIMA, for over a 1300 training set and range for all the params being 0,1 and 2. But this process is taking over ...
0
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1answer
19 views

ML technique to predict next year output based on text quantities [closed]

I have a random data that I would like to predict how much a quantity will be in 2020. The data looks like this: ...
1
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0answers
98 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 ...
0
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0answers
36 views

Curve fitting with R

I was wondering if there is a package in R for fitting a non-symmetrical bell-shape curve?
3
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1answer
63 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 ...
1
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0answers
141 views

Monthly trend with fb prophet

I have monthly data with month/year in one column and price on another. I would like to get a yearly trend with fb prophet library in python (how to use monthly data with the library is explained at ...
2
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1answer
54 views

Forecasting energy consumption with no historical data when there is a trend

I want to forecast new customers' energy consumption. Let's say I can construct a set of attributes to describe new and existing customers (e.g. size of business, type of business etc.) and I have the ...
2
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0answers
378 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 ...
2
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0answers
172 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 ...
1
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0answers
26 views

What is the correct format of the test input for LSTM neural network? [closed]

I have learned some tutorials for LSTM time series prediction. According to that tutorials, My training data format would be like, NOTE : This data is only for example. this is not the real data. <...
1
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1answer
71 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, ...
1
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2answers
172 views

Multivariate VAR model: ValueError: x already contains a constant

I have already read this question and the associated answer. I have removed any 'all zero' columns, as recommended in the answer. I have 3,169 columns remaining. ...
0
votes
1answer
17 views

Autoregression with multiple factors

I am not sure if this is the right place to ask this question. Anyway, I am working on a Forecasting using spending data. Using autoregression, I am able to predict the following number decently well ...
1
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0answers
36 views

What kind of algorithm should I use to build ML model that can predict just next reoccurence of an event in the future (at irregular time interval)?

I'm quite new to machine learning and statistics. I've a dataset from some ecommerce sale's history. It's almost 2k instances, and features include personId (string), productCategory (string/...
1
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1answer
38 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 ...
1
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1answer
352 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: ...
1
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2answers
35 views

How to predict weather? [closed]

I want to know how does one predict day to day weathers. Like what are the factors that must be known to predict day to day weathers using a NN. Few factors I could think of are: Humidity Weather ...
1
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0answers
26 views

Can ARIMA be applied on a dataset of few months?

I have dataset of a few months of (time series) electrical load usage of multiple users. Can/Should ARIMA be applied for load forecasting considering that its not much data?
3
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2answers
515 views

VAR model ValueError: x already contains a constant

I'm using VAR model for multivariate time series. The structure is that although each variable is a linear function of past lags of itself and past lags of the other variables, one and/or two of the ...
-1
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2answers
413 views

forecasting revenue

Does anyone have any recommendations on how I would go about forecasting Microsofts revenue using python + time series or ML (recommended techniques e.g Random-forest). (I have past revenue and share ...
6
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1answer
2k views

Can Reinforcement learning be applied for time series forecasting?

Can Reinforcement learning be applied for time series forecasting?
1
vote
1answer
190 views

demand forecast for B2B

I am attempting to create a demand forecasting model in python to predict future sales of a particular category of product, using historical sales data. We are a B2B company, which means that we ...
1
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1answer
40 views

How to utilize user survey answers and the actual usage in forecasting power usage using LSTM?

I have the pre-trial survey and post-trial survey conducted of around 5000 users for Smart Meter installation. With this I have power usage reading recorded every 30 min for around one and a half ...
0
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1answer
130 views

A timestep's prediction depends on future data

Consider an LSTM model with 100 timesteps, each of which with input and target data. Let f(99) be the function mapping the input data of the 99th timestep and hidden state of the 98th timestep to the ...
1
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0answers
34 views

Analysing spikes in demand to forecast future demand

I am trying to develop a system to predict future demand for different products at my firm. Currently, I have about two years of weekly sales data of a single product. My target is to predict the ...
1
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0answers
20 views

How to decide which forecasting model to use? [closed]

So I have a sales forecasting problem where I have 3 years worth of data about weekly sales of a certain company. There are 3 types of retail stores in that company, Type A, B, and C. There are a ...
1
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0answers
90 views

Predict the probability that a customer buy today

My company sells a single product that is a commodity. If you buy it, I can be sure that you will buy it again in the near future from me or from my competitors. The demand is affected by the weather. ...
4
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1answer
825 views

Time series forecasting using multiple time series as training data

I am trying to forecast the total attendance (ie. the number of entrances, which is also the number of tickets bought) of a festival just two days after it started. That is, knowing how many people ...
0
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3answers
165 views

Forecast vs Prediction: What is the difference?

I use the two terms as follows: A prediction model gets features (which can be a time series) as input and gives a fixed-length output (might be multiple values, but "atomic" in some sense) Examples:...
2
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1answer
70 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 ...
2
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0answers
1k views
2
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1answer
80 views

How to predict next year's gross revenue given this year's data?

I have the following dataset (.csv format) which contains: 100 columns: $\textbf{Period}$ (in years, e.g. 2017, 2018, ..., 2028), $\textbf{Gross Revenue}$, $\textbf{Region}$ (e.g. APAC, NEMEA, etc), ...
2
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1answer
326 views

how to decide categorical variables for prediction

I have a dataset that contains weekly sales for stores and categories. It looks like this: I would like to apply gradient boosting method to predict weekly sales. My question is: Should I create ...
1
vote
1answer
25 views

Last cell in recurrent network always the most accurate

I am using a recurrent network for time series forecasting. The prediction from the last cell in the network always seems to be the most accurate. For example if I have 20 cells (so my input samples ...
1
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0answers
38 views

What values should I choose for P and Q?

I am trying to forecast energy data generated by LoadProfileGenerater. This data is generated every half hour for 2 years. I am following this tutorial. I have checked it for stationarity using ...
10
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1answer
5k views

How to Predict the future values of time horizon with Keras?

I just built this LSTM neural network with Keras ...
4
votes
1answer
69 views

What are the prerequisites before running Holt Winters Model?

I just read Demand-Driven Forecasting: A Structured Approach to Forecasting(Wiley and SAS Business Series) and have a few doubts in Holt-Winters Model: 1) Unlike OLS Regression Modeling technique or ...