Comments on: Stock Market Prediction – Adjusting Time Series Prediction Intervals in Python https://www.relataly.com/changing-prediction-intervals-for-time-series-forecasting-models/169/ The Business AI Blog Sat, 27 May 2023 10:18:10 +0000 hourly 1 https://wordpress.org/?v=6.9.4 By: David campbell https://www.relataly.com/changing-prediction-intervals-for-time-series-forecasting-models/169/#comment-203 Sat, 28 Aug 2021 14:16:58 +0000 http://www.relataly.com/?p=169#comment-203 hi there,
i did not get the code. first you tried to convert daily data into week data. but in neural model, you utilized daily data. Also did prediction on daily x_test data. then how you predicted next week data?

]]> By: Ed https://www.relataly.com/changing-prediction-intervals-for-time-series-forecasting-models/169/#comment-199 Wed, 11 Aug 2021 05:33:09 +0000 http://www.relataly.com/?p=169#comment-199 in the daily timeframe you could also forecast a price 7 days (or 7 bars ) in the future by adjusting the samples. For instance, see below. I tested it and it works. So it just forecasts 1 bar but 7 bars ahead instead of just 1 bar ahead. Of course the length of the array pred_price_unscaled will also be nfut bars shorter.

Let me know what you think

nfut = 7

def partition_dataset(sequence_length, train_df):
x, y = [], []
data_len = train_df.shape[0]
for i in range(sequence_length, data_len – nfut + 1):
x.append(train_df[i-sequence_length:i,:]) #contains sequence_length values 0-sequence_length * columsn
y.append(train_df[i + nfut – 1, index_Close]) #contains the prediction values for validation (3rd column = Close), for single-step prediction

# Convert the x and y to numpy arrays
x = np.array(x)
y = np.array(y)
return x, y

]]>